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Previous Seminars 2000-2005

Spring 2007; Autumn 2006; Summer 2006; Spring 2006; Autumn 2005; Summer 2005; Spring 2005; Autumn 2004; Summer 2004; Spring 2004; Autumn 2003; Summer 2003; Spring 2003; Autumn 2002; Summer 2002Spring 2002; Spring 2001; Autumn 2000

Spring 2007

Dr. Tony Prescott, University of Sheffield, Computational Ethology of an Active Sense System: Biological and Robotic Investigations of Tactile Perception in the Rat

Sensing in animals and in robots is often an active process in which the position and orientation of the sensory apparatus is controlled so as to enhance the agent's capacity to obtain behaviourally-relevant information. The rat whisker system is considered to be a paradigmatic example of such an "active sense system", and together with researchers at Bristol Robotics Laboratory my research group has been conducting novel ethological, computational neuroscience, and robotic investigations of whisking viewed an active tactile sense. A key focus of our research has been on the feedback control strategies that the rat uses to position its whiskers whilst exploring the world. Results from the study of both natural and artificial whisking systems will be presented to show how active control can increase both the quantity and quality of information acquired by the whiskers. Humans, and other primates, also use active control strategies to position the tactile sensory surfaces on the fingertips. Therefore our findings may be relevant to the wider understanding of the mammalian sense of touch as well as to the design of active sense systems for autonomous robots.

Dr Mohamed Henini, School of Physics & Astronomy, University of Nottingham, Molecular Beam Epitaxy from Research to Mass-Production

Most physics undergraduates will have encountered the potential well as their first problem in undergraduate quantum mechanics. New crystal growth technique such as molecular beam epitaxy has made it possible to produce quantum wells in practice. This sophisticated technology for the growth of high quality epitaxial layers of compound semiconductor materials on single crystal semiconductor substrates is becoming increasingly important for the development of the semiconductor electronics industry. In this article I will review the main achievements of MBE both in fundamental research and manufacturing.

Alessandro Bassi, Hitachi, Data Management Challenges in the Next Decade

One of the main challenges, if not the main one, we will have to face in the coming future is how to manage an ever growing amount of data. It seems hard to sustain that our current storing and networking capabilities will be able to handle what Tony Hey, former director of the UK e-Science program, now at Microsoft, calls "data deluge". The emergence of new storage networking technologies such as Fibre Channel and the ramp-up of the Internet in the mid-nineties were announcing a much longer-term transformation of data access and management practices. Whilst the Internet use was somehow homogeneising the access to web-based data repositories and services, inside the data centres the infrastructure complexity was and is still increasing in order to fulfil business application requirements. As data repositories in operations grow rapidly and in certain cases exponentially, IT users want more flexibility to access, filter, update, distribute or analyse their data as well as a simplification of the management interface and tools. Is the current TCP/IP mechanism up to this challenge?

Dr. Youssef Hamadi, Constraint Reasoning Group, Microsoft at Cambridge, Performance prediction and automated tuning of randomized and parametric algorithms.

Machine learning can be utilized to build models that predict the runtime of search algorithms for hard combinatorial problems. Such empirical hardness models have previously been studied for complete, deterministic search algorithms. In this work, we demonstrate that such models can also make surprisingly accurate predictions of the run-time distributions of incomplete and randomized search methods, such as stochastic local search algorithms. We also show for the first time how information about an algorithm's parameter settings can be incorporated into a model, and how such models can be used to automatically adjust the algorithm's parameters on a per-instance basis in order to optimize its performance. Empirical results for Novelty+ and SAPS on structured and unstructured SAT instances show very good predictive performance and significant speedups of our automatically determined parameter settings when compared to the default and best fixed distribution-specific parameter settings.

Prof. Lik-Kwan Shark, University of Central Lancashire, Image Matching: From Large Aerostructure to Deformable Anatomy

Image matching is concerned with the alignment of multiple images of the same scene/object acquired from different times/views/sensors. A wide spectrum of issues related to applied image matching will be covered in the talk, using examples from automatic matching of non-destructive testing images of large aerostructures with 3D CAD model to assist structure integrity evaluation, to automatic matching of radiotherapy images of patients to assist cancer treatment. Drawn from a number of recent collaborative research projects with leading aerospace companies in Europe (such as BAE Systems and EADS) and major NHS Trusts in the North-West (such as Christie Hospital), Professor Shark will present several novel image matching techniques which include 2D-2D feature based geometry matching based on higher geometric primitives, 2D-3D matching based on silhouette via spherical projection combined with the iterative closest point algorithm, and elastic image matching based on adaptive deformation controlled by anatomical structures.

Gordon Wyeth, Director, Robotics Laboratory, University of Queensland, Australia, Robotics Research at the University of Queensland

The robotics laboratory at the University of Queensland has conducted a variety of projects over the past five years. This talk will present an overview of the activities of the lab in the following areas:

Humanoid Robot: We have developed a functional 1.2m tall humanoid robot, called "GuRoo". This robot has been an interesting mechantronics project, and has been used as a testbed for investigations in biologically inspired robot control techniques. Our humanoid came fourth in the world in robot soccer in 2005.

Video-conferencing Robots: Five "Ambassador" robots were built and delivered to SAP as prototypes of highly interactive video conferencing robots. These robots are now in operation in Australia, Singapore and Germany and have been presented to the SAP international research meeting where they were enthusiastically received.

SLAM: We have demonstrated a biologically inspired solution to this widely-held "holy grail" of autonomous robot problems. The RatSLAM system has been demonstrated mapping large, unmodified indoor and outdoor areas using colour vision as the only sensor.

Autonomous Helicopters: Working in collaboration with CSIRO, we have developed a system for learning elements of helicopter flight, particularly focussing on the challenging problem of hover. We demonstrated autonomous hover using both adaptive and non-adaptive methods.

Small Size Robot Soccer: Our team of soccer robots has demonstrated some of the most sophisticated multi-robot intelligence and control in the world. The robots achieve sophisticated coordination while moving at speeds of up to 2 m/s. They can accurately pass and shoot the ball at over 4 m/s. This outstanding performance has led the team to second place in the robot soccer World Cup in 2003 and 2004, beaten by a single goal in both case

Prof. Kenzo Nonami, Chiba University, Japan, Autonomous Control of MAV, UAV and UGV

Autonomous control for MAVs like Micro-Flying Robot (µFR) will be presented. In case of natural disaster like earthquake, MAVs will be very effective for surveying the site and environment in dangerous area or narrow space where human cannot access safely. In addition, it will be a help to prevent secondary disaster.

This talk is concerned with autonomous hovering control, guidance control of µFR. Next, the present state of research and development for civil use autonomous uninhabited aircraft like UAVs which are bigger than MAVs is introduced.

Many autonomous flight movies are introduced. In particular, the research and development in the world and in Japan, and the subjects and prospects for control and operation systems of civil use autonomous UAVs are presented. Also, the trial of formation control will be discussed.

Finally, the legged robots which have six legs as UGVs(Unmanned Ground Vehicle) are introduced with vision based locomotion.

Prof. Leslie S. Smith, University of Stirling, Neuroinformatics: The CARMEN Project

CARMEN is a recently started e-Science pilot project on neuroinformatics: that is on the use of computing technology for analysing neural signals. It is an interdisciplinary project involving 11 Universities in the UK, and the investigators include experimental neurophysiologists, computational neurophysiologists, as well as computer scientists. The seminar will start with an overview of the project, and then the particular research ongoing at Stirling will be discussed this concentrates on techniques for detecting action potentials ('spikes') and associating them with specific neurons (spike sorting) in extracellular recordings.

Prof. Jin-Kao Hao, LERIA, University of Angers, France, New Heuristics for Maximum Parsimony Phylogenetic Tree Reconstruction

Phylogeny can be defined as the reconstruction of the evolutionary history of a set of species identified by sequences of molecular or morphological characters. The Maximum Parsimony problem aims at reconstructing a phylogenetic tree according to the criterion of minimizing the number of genetic transformations. To solve this difficult problem, a number of heuristic methods have been developed, many of them being based on the local search paradigm. In this talk, we will present two new heuristics for the Maximum Parsimony problem. The first one, called Progressive Neighbourhood Search (PNS), is a descent method that is based on a parametric and progressively decreasing neighbourhood [1]. The second heuristic method is a hybrid genetic algorithm, which combines the Progressive Neighbourhood Search and a Distance-based Information Preservation (DiBIP) Tree Crossover [2]. We will show experimental results obtained with these heuristics on a number of benchmarks of the literature.

Prof. Eduardo Miranda, University of Plymouth, Disciplinary Routes to Innovation in Computer Music Research

Sophisticated tools for the creation, generation and dissemination of music have established clear synergies between music and leisure industries, technology within art, the creative industries and the creative economy of the twenty first century. This can be seen in internet radio, internet self publishing, easy email access to geographically distributed audiences with minority interests, podcasts, mobile technologies and digital archives. This lecture will demonstrate how research combining evolutionary computation, neuroscience and music is leading to the development of new intelligent interactive systems that will be able to evolve their own rules for musical composition and the ability to interact with musicians and listeners in much more sophisticated ways than the present ones can do. We predict the emergence of new kinds of music content, most of which will be generated on the fly, requiring new modes of representation, access and interaction. The lecture will be illustrated with snapshots of ongoing research at the University of Plymouth on modelling musical evolution, interactive music systems and brain-computer music interfacing.

Dr. Krzysztof R. Apt, CWI and University of Amsterdam, The Netherlands, Infinite Qualitative Simulations by Means of Constraint Programming

We introduce a constraint-based framework for studying infinite qualitative simulations concerned with contingencies such as time, space, shape, size, abstracted into a finite set of qualitative relations. To define the simulations we combine constraints that formalize the background knowledge concerned with qualitative reasoning with appropriate inter-state constraints that are formulated using linear temporal logic. We implemented this approach in the constraint programming system Eclipse by drawing on the ideas from bounded model checking. The implementation became realistic only after several rounds of optimizations and experimentation with various heuristics. The resulting system allows us to test and modify the problem specifications in a straightforward way and to combine various knowledge aspects. To demonstrate the expressiveness and simplicity of this approach we discuss in detail two examples: a navigation problem and a simulation of juggling.

Autumn Term 2006

Dr. Phillip McKerrow, University of Wollongong, Australia, Ultrasonic sensing for mobile robotics

Em. Professor Leslie Kay has been comercialising ultrasonic mobility aids for blind people for 40 years. These sensing systems use a Continuously Transmitted Frequency Modulated (CTFM) signal. Dr McKerrow has used these sensors to classify objects, including leafy plants, for landmark navigation of mobile robots. He believes that the key to successful landmark navigation is in the quality of the sensing of the landmarks. Classification involves both identifying the object and measuring the correctness of the classification. He has developed an outdoor 4-wheel drive mobile robot and is investigating using surface roughness as a landmark in conjunction with a Fuzzy map of paths. As echolocation is used by bats to navigate in flight, his research has naturally progressed to the development of an indoor aerial robot for use in disaster search.

Dr. John Levine, University of Strathclyde, Evolving Intelligence

Designing intelligent behaviour is very hard; recognising it is much easier. Furthermore, given two behaviours we can generally recognise the more intelligent one. If a population of diverse behaviours can be created, we can use artificial evolution to find behaviours which are more intelligent. Over time, intelligent behaviours will emerge by competition with each other, rather than needing to be designed. This simple and intuitive idea can be applied to a wide range of tasks which need intelligence, such as games playing, robotic control, planning, problem solving - indeed, anything which involves perceptions, decisions and action. In the talk I will present some diverse examples of evolution used to produce intelligent behaviours, including some recent examples from the L2Plan (Learn to Plan) and EvoTanks (evolving game playing agents) projects at Strathclyde. I will also speak on the limitations of the current process: it evolves agents which are specialised to the task in hand and are usually only reactive rather than reflective. Can we go beyond this and evolve true intelligence?

Dr. Jonathan M Garibaldi, University of Nottingham, Towards Modelling the Variation in Human Decision Making

The purpose of developing an expert system is to encapsulate knowledge and expertise of human experts in the particular domain. However, human experts exhibit variation in their decision making. Variation may occur among the decisions of a panel of human experts (inter-expert variability), as well as in the decisions of an individual expert over time (intra- expert variability). A conventional fuzzy expert system (FES) was developed to assess the health of infants immediately after birth by analysis of the biochemical status of lood taken from an infant's umbilical cord (umbilical acid-base analysis). A database of fifty difficult to interpret cases had previously been collected and six experts in umbilical acid-base analysis had ranked these cases from `worst' to `best'. It was observed that the experts' conclusions varied both among themselves and from their previous conclusions, whereas the FES produced the same output each time the same input was given. The source of this variation was suspected to lie in minor variations of the interpretations of linguistic terms used in the rules. In order to model this, experiments were carried out by introducing small changes in the membership functions associated with the linguistic terms. Thus it was possible to explore the relationship between the vagueness of these terms and the variation in the overall decision making (the conclusions reached by means of the fuzzy inferencing). The term `non-stationary' was introduced to describe such time-varying fuzzy systems. In this seminar, Dr Garibaldi will detail the specific decision making problem, will describe the fuzzy expert system created and then will present the novel techniques utilised for introducing variation into fuzzy expert systems. Finally, results will be presented of comparisons between this novel fuzzy inferencing technique and human decision making.

Professor Sameer Singh, University of Loughborough, Developing Autonomous Systems with Computer Vision Technology: An Overview of Research at RSI, Loughborough University

This talk will provide a broad overview of our research in the area of developing computationally intelligent computer vision based autonomous systems. The overview will discuss research problems and key research results in the areas of biomedical imaging, scene analysis and novelty detection, security and visual surveillance, machine vision based industrial inspection, digital photography, autonomous mobile systems, and multi-resolution pattern recognition. The overview is intended to give a broad appreciation to the audience on the need for such systems in their respective application areas, how to develop the methodology for solving such complex problems, appropriate experimental design and key challenges faced when developing real solutions. A number of experimental results will be visually presented.

Dr. Heiko Hoffmann, University of Edinburgh, Using Sensorimotor Models to Make Sense out of Sensory Input

Sensory input, such as the activation of the rods and cones on the retina or the activation of tactile sensors on the hand, does not resemble the geometric properties of the objects observed (for example, lines are not mapped onto lines on the retina, or the tactile patterns obtained from holding a ball do not resemble a spherical shape). Yet, how do we understand, for example, the rotational symmetry upon seeing or touching a ball and the meaning of a dead-end when looking down a corridor? To shed light into these questions, I present experiments in which a robot learns a sensorimotor model by observing the sensory consequences of the robot's actions. Using this model, the robot can link object properties and symmetries with sensory input and has thus not only a means to detect such properties, but can also assign to them a behavioural meaning.

Prof. Shangkai Gao, Tsinghua University, Beijing, China, Non-invasive Brain-Computer Interfaces - On-line System Development at Tsinghua University

A brain-computer interface (BCI) is a communication system that does not depend on the brain's normal pathways of peripheral nerves and muscles. Non-invasive BCIs are implemented based on the decoding of brain waves recorded on the surface of scalp, which are probably the most acceptable systems to BCI users. Two non-invasive BCI systems recently developed at Tsinghua University, a brain-controlled telephone and a mind-controlled robot, will be presented with demo videos.

Dr Nick Antonopoulos, University of Surrey, Performance and Scalability Optimisation of P2P Networks through Adaptive Cooperative Network Construction

P2P Networks are dynamic distributed systems which are increasingly being used as a methodology for organising and efficiently retrieving information and resources between end users on a large scale. Current research has focused on optimising the searching process through caching, topology adaptation, hashing and probabilistic heuristics. In this talk I will firstly provide a gentle introduction to P2P networking and secondly I'll present the main research conducted in this field at the Department of Computing of the University of Surrey. Our work has focused on two specific aspects of P2P optimisation: Firstly on using the network construction process in order to keep typical structured P2P overlays just big enough to handle their current workload and secondly on linking independent P2P networks together so that they can share capacity on demand. The former principle enhances the search process as the actual size of the network determines the actual data traffic each user query produces and therefore smaller networks exhibit less traffic for the same number of queries. The latter enables a P2P network to dynamically acquire capacity from other under-utilised networks when it experiences peaks in its workload and therefore makes it more scalable in terms of the number of user queries it can process. In this context, the talk will show an overview of the design of two systems which are based on the above principles and a set of simulation results that show their effectiveness in enhancing the performance and scalability of P2P networks.

Prof Franco Callegati, University of Bologna, Italy.  Engineering Optical Buffers with Delay Lines: Design and Modelling

This seminar refers to the problem of congestion resolution in all optical burst or packet switched networks.

The seminar presents the basic concepts of engineering optical buffers realized with Delay Lines. The principle of operation of a delay buffer is presented and compared to conventional queuing. The related problems of performance analysis and engineering are considered, presenting a review of analytical models, with particular emphasis on the case of asynchronous variable length packets.

Finally examples of engineering of delay buffers in various scenarios of optical networking are presented, with particular reference to a Dense WDM scenario, focusing on scheduling algorithms that exploit buffering in conjunction with load balancing on the wavelength set.

The aim is that of explaining which are the critical parameters to be dimensioned, showing at what extent delay buffer may be effective to solve congestion in all optical network nodes.

Dr. Jose A. Lozano, University of the Basque Country (Spain), Probabilistic Graphical Models in Optimization: Applications in Protein Folding and Design

We present two different ways of using probabilistic graphical models in combinatorial optimization: estimation of distribution algorithms and message passing algorithms. Several instances of these two sets of algorithms will be applied to solve problems in the protein field. Particularly, we deal with protein folding and protein design. In the former problem two different protein models will be used, the H-P model and side chain placement prediction. The advantages of using optimization algorithms based on probabilistic graphical models in these two problems will be highlighted.

Dr. Dariusz Kowalski, University of Liverpool, Distributed Fault-Tolerant Algorithms with Small Communication

Distributed systems contain several procedures supporting the communication between computers, synchronisation, scheduling tasks, and many other basic operations involving more than one machine. These procedures are implemented independently on the network layer, and so they need to tolerate various types of failures which may occur during the execution, including crashes, omissions, malicious bahaviour. This talk is about fundamental fault-tolerant primitives in message-passing synchronous environment, such as gossip, consensus, performing tasks. Presented solutions are optimized simultaneously for the time and communication complexities, in the presence of various types of failures. As an example, in the Consensus problem, each processor starts with its input value and the goal is to have all processors agree on exactly one value among the inputs. We show that it can be solved in time proportional to the number of failures and sending asymptotically n\log^5 n messages, where n stands for the number of processors in the system. We also discuss some future research directions connected with presented topics.

Prof. Eduardo Miranda, University of Plymouth, Trans-Disciplinary Routes to Innovation in Computer Music Research

Sophisticated tools for the creation, generation and dissemination of music have established clear synergies between music and leisure industries, technology within art, the creative industries and the creative economy of the twenty first century. This can be seen in internet radio, internet self publishing, easy email access to geographically distributed audiences with minority interests, podcasts, mobile technologies and digital archives. This lecture will demonstrate how research combining evolutionary computation, neuroscience and music is leading to the development of new intelligent interactive systems that will be able to evolve their own rules for musical composition and the ability to interact with musicians and listeners in much more sophisticated ways than the present ones can do. We predict the emergence of new kinds of music content, most of which will be generated on the fly, requiring new modes of representation, access and interaction. The lecture will be illustrated with snapshots of ongoing research at the University of Plymouth on modelling musical evolution, interactive music systems and brain-computer music interfacing.

Prof. Silvano Cincotti, University of Genoa (Italy). Power Exchange

Dr. Susan Stuart, University of Glasgow, Bodily Consciousness and Kinaesthetic Imagination

Imagination is commonly thought to amount to nothing more than a creative faculty. But imagination as bodily expectation can be revealed through examination of the imagination’s productive, bodily, character, that aspect of the mind that extrapolates through bodily consciousness or experience an anticipation or, let us say, an expectation of how our world will continue to be from moment to moment, from sensation to sensation. It is a notion of imagination that is fundamental to any conscious experiencing system, for it is through the smooth functioning of this imagination that we are able: (i) to have unconscious expectations about how our world will be with regard to each of our senses and (ii) to recognise change when our sensory expectations are not realised. Perhaps most importantly it is from this power of the imagination, to build up unconscious sensory expectations and recognise when they are not realised, that we are able to develop our sense of the passage of time. Thus, our sense of the passage of time is, at base, physiological and unconscious, and derived from our plenisentient and dynamic coupling with our environment.

Pierre Roduit, École Polytechnique Fédérale de Lausanne, Switzerland Using a Point Distribution Model to extract behavioral features from the trajectories of a mobile robot

The work that will be presented results from a collaboration between the Vision Group of the Laboratoire de Production Microtechnique
(LPM) and the Swarm Intelligent Systems Group (SWIS), both at the École Polytechnique Fédérale de Lausanne in Switzerland. Combining the knowledge and expertise of these groups in robotics (SWIS) and in computer vision
(LPM) allows us to use mobile robots as trajectory generators to test models in computer vision and to develop methods of analysis for mobile-robot controllers based on their trajectories. The Point Distribution Model is a deformable template widely used in computer vision to model complex shapes. Applying it to the trajectories of a mobile robot provides an effective tool for analyzing them, as behavioral differences and hardware heterogeneities can be classified in the PDM-transformed space. Moreover, the quantitative dissimilarities observed in the PDM space can also be transformed back into physical space, making it easier for us to develop an understanding of their spatial meaning.

Prof. Ian Marshall, University of Kent, Autonomous Systems and Their Role in Supporting Scientific Discovery

Summer Term 2006

Dr Luciano Floridi, University of Oxford, The Informational Nature of Reality

In recent years, there has been a renewed and growing interest in ontological issues. One of the classic questions that has resurfaced is whether the ultimate nature of reality might be digital (discrete) or analogue (continuous). Digital Ontology (and its corollary thesis of Pancomputationalism) and Informational Structuralism seek to provide an answer to this fundamental and very old question, by relying on original and innovative analyses in terms of computational systems. They are fellow travellers, which share a similar outlook but differ in the conclusions reached. In this talk, I shall first provide a brief introduction to a digital approach to ontology. I shall then argue that Digital Ontology is mistaken insofar as it favours a one-sided interpretation of the ultimate nature of reality. I shall present a thought experiment against the dichotomy “digital vs. analogue” in order to argue that either (a) the dichotomy is misapplied because of a confusion regarding the nature of the object under analysis or (b) if properly applied, it runs into counterintuitive consequences. This leaves Informational Structuralism—according to which it is a matter of levels of abstraction whether reality is to be described as digital or analogue—as a more convincing option. Informational Structuralism may not be the only game in town, but it is the only one that is metaphysically safe to play.

Professor Mahdi Mahfouf, University of Sheffield, From Biological Systems to Materials Processing: Investigations into the Contributions of Intelligent Systems Modelling Optimisation, and Control

From Biological Systems to Materials Processing: Investigations into the Contributions of Intelligent Systems Modelling Optimisation, and Control
There is no doubt that clinical medicine is among the last areas where systems engineering tools, such as modelling, system identification, signal processing, and feedback control, have proliferated. The real reason behind such a change of attitude rests in the fact that clinical medicine had to be convinced first that all the above systems engineering tools can be effective and reliable but most importantly safe. During this seminar, the speaker will review his research experiences in tackling some of the problems and complexities embedded in the human system when looking particularly at anaesthesia and intensive care therapies. In contrast to patient care, the speaker will also emphasise the generic nature of his research by explaining how some of his previously developed techniques for knowledge extraction/fusion, and control have been extended to the metal industry which, while apparently different from the patient care industry, seems to be driven by similar requirements/priorities.

Dr James Borrett, Global Optimum Ltd, Relating Constraint Based Problem Solving Techniques to the Real World Applications

There are many techniques being researched for solving optimisation problems. These range from classic complete search algorithms to complex variations of stochastic approaches. The selection of the most suitable algorithm, coupled with the perennial issue of how to model a problem effectively means that choosing the right algorithm/model combination is extremely important. The difference between getting it right or wrong can result in order of magnitude performance variations. This has been a hot topic in research since the mid 1990's.

In this talk I shall be outlining some examples of constraint based optimisation problems which occur in industry, how they might be tackled, and some of the issues which can arise. These will include transport scheduling, personnel scheduling and port demurrage minimisation. There will also be the chance of winning a mystery prize at the end of the talk.

Dr Ralf Herbrich, Microsoft Research in Cambridge, TrueSkill(TM): Bayesian Skill Ranking

The TrueSkill ranking system is a skill based ranking system for Xbox Live developed at Microsoft Research. The purpose of a ranking system is to both identify and track the skills of gamers in a game (mode) in order to be able to match them into competitive matches. The TrueSkill ranking system only uses the final standings of all teams in a game in order to update the skill estimates (ranks) of all gamers playing in this game. Ranking systems have been proposed for many sports but possibly the most prominent ranking system in use today is ELO.

Dr Tao Geng, University of Essex, RunBot: The World's Fastest Dynamic Biped Walking Robot

In this talk I will present my design and experiments on a planar biped robot (RunBot) under the control of a sensor-driven neuronal controller. This design has some special mechanical features, such as small curved feet allowing rolling action and a properly positioned centre of mass, which facilitate fast walking. The sensor-driven controller is built with biologically inspired sensor- and motor-neuron models, and does not employ any kind of position or trajectory-tracking control algorithm.

Instead, it allows the RunBot to exploit its own natural dynamics during critical stages of its walking gait cycle. Due to the interaction between the neuronal controller and the properly designed mechanics of the robot, RunBot can realize stable dynamic walking gaits in a large domain of the neuronal parameters. In addition, this structure allows the use of a policy gradient reinforcement learning algorithm to tune the parameters of the sensor-driven controller in real-time, during walking. In this way RunBot can reach a relative speed of 3.5 leg-lengths per second after only a few minutes of online learning, which is tremendously faster than that of any other biped robots, and is also comparable to the fastest relative speed of human walking. News about RunBot.

Spring 2006

Professor Maria Stokes University of Southampton, Brain-computer Interfacing in Rehabilitation

Brain-computer interfacing (BCI) systems can enable a person to control a computer using thoughts, detected by electroencephalography (EEG). The field of BCI research is at a relatively early stage of producing reliable, robust systems that are widely accessible for everyday use. The clinical implications of this technology in different areas of rehabilitation will be reviewed.

Several BCI research groups are developing systems for communication and environmental control in people with severe disabilities. Another dimension to BCI in rehabilitation research is using the technology for investigating mechanisms of dysfunction, which would aid diagnosis and provide a screening tool to assess a person’s ability to access BCI. Such application would also extend the use of BCI as an aid to re-training of movement and language.

Signal processing techniques continue to be refined and are improving the accuracy and reliability of BCI technology but translation into routine clinical use is limited by several factors influencing accessibility and compliance (e.g. difficult user tasks requiring long training periods, number of recording channels etc.). Surface or implanted recording devices can be used. It is suggested that for transient use in certain areas of rehabilitation, surface electrodes are appropriate.

The Southampton BCI Research Programme is a joint venture between the School of Health Professions & Rehabilitation Sciences and the Institute of Sound & Vibration Research, and is embedded in the University’s Life Sciences Interface Forum. The Programme involves collaborative research between various disciplines in rehabilitation (physiotherapy, speech and language therapy, medicine, psychology) and engineering, computing and biological sciences. An important aim is to bridge the gap between major technological advances and the relatively limited success in practical applications of BCI systems.

Dr Jonathan Rowe, University of Birmingham, Propagation time in stochastic communication networks

Dynamical processes taking place on networks have received much attention in recent years, especially on various models of random graphs (including “small world” and “scale free” networks). They model a variety of henomena, including the spread of information on the Internet; the outbreak of epidemics in a spatially structured population; and communication between randomly dispersed processors in an ad hoc wireless network. Typically, research has concentrated on the existence and size of a large connected component (representing, say, the size of the epidemic) in a percolation model, or uses differential equations to study the dynamics using a mean-field approximation in an infinite graph. Here we investigate the time taken for information to propagate from a single source through a finite network, as a function of the number of nodes and the network topology. We assume that time is discrete, and that nodes attempt to transmit to their neighbours in parallel, with a given probability of success. We solve this problem exactly for several specific topologies, and use a large-deviation theorem to derive general asymptotic bounds, which we use, for example, to show that a scale-free network has propagation time logarithmic in the number of nodes, and inversely proportional to the transmission probability.

Dr James Anderson, University of Reading, The Perspex Machine

The perspex machine unifies the Turing machine with geometry so that any symbolic computation can be performed geometrically, though some geometrical computations have no symbolic counterpart. Even when simulated approximately on a digital computer, the Turing computable, geometrical properties of the perspex machine are useful. There will be a concise presentation of three properties of the Perspex machine, followed by a discussion that can be as wide ranging, or as focused, as the audience desires. 1) The co-ordinates used by the perspex machine form a total arithmetic so there are no arithmetical exceptions in the machine. This means that no error-handling is needed in the perspex machine and that general software can be more secure. The total arithmetic used illustrates a fundamental error in the way IEEE floating-point arithmetic handles NaN. 2) In general, a Turing computable sequence of numbers with less than quadratic convergence is non-monotonic. But then a general, Turing computable, perspex computation with less than quadratic convergence is non-monotonic. But then general, physical operations with less than quadratic convergence are non-monotonic. Plausible examples include: punctuated equilibrium in genetic evolution, the emergence of biological species, and paradigm shifts in science. 3) The perspex machine is implemented in terms of a single instruction that has the geometry of an artificial neuron. This neuron will be illustrated via a compiler that can compile a sub-set of the C programming language into networks of perspex neurons. In principle, any Turing program could be compiled into a network of perspex neurons.

Dr Daniel Polani, University of Hertfordshire, Organization of Information Flows in the Perception-Action Loop of Agents

Information is an essential and omnipresent resource and has long been suspected as a major factor shaping the emergence of intelligence in animals and as a guideline to construct artificial intelligent systems. In search for fundamental principles guiding the self-organization of neural networks, Linsker (1988) formulated a number of information-theoretic hypotheses. His model (and most of its successors) was purely passive. However, recent work by Touchette and Lloyd (2000) extending early work by Ashby (1953), as well as some work by Polani et al. (2001) has shown that actions can be incorporated into the information- theoretical analysis.

As was found by Klyubin et al. (2004), incorporating actions into an information-theoretic formalization of the perception action-loop of agents has dramatic consequences in terms of self-organization capabilities of the processing system. As opposed to Linsker's model which required some significant pre-structuring of its neural network, this new model makes only minimal assumptions about the information processing architecture. The agent's "embodiment", i.e. the coupling of its sensors and actuators to the environment, is sufficient to give rise to structured pattern detectors driven by optimization principles applied to the information flow in the system.

In the present talk, we will motivate Shannon information as a primary resource of information processing, introduce a model which allows to consider agents purely in terms of information and show how this model gives rise to the aforementioned observations. If there is time, the talk will discuss the use of information-theoretic methods to structure the information processing also in real robot systems.

Dr Andrew Davison, Imperial College London, Real-Time SLAM with a Single Camera

Recently I have generalised the Simultaneous Localisation and Mapping (SLAM) methodology, developed to enable mobile robots to navigate in unknown environments, to demonstrate real-time 3D motion estimation and visual scene mapping with an agile single camera.  A webcam attached to a laptop becomes a high-performance position and mapping sensor, which can be used by advanced mobile robots or coupled to a wearable computer for personal localisation. This research improves on the previous state of the art in 3D computer vision research, where emphasis had been on off-line reconstruction algorithms inspired by photogrammetry. When hard real-time performance (e.g. 30Hz) is required, the limited processing resources of practical computers mean that fundamental issues of uncertainty propagation and selective visual attention must be addressed via the rigorous application of methods from probabilistic inference and information theory. The result is an algorithm which harnesses background knowledge of the scenario to obtain maximum value from visual processing. Practically, this technology has a host of interesting potential applications in many areas of robotics, wearable computing and augmented reality. The presentation will include a live demonstration.

Dr John McCall, The Robert Gordon University, EDAs and Markov Networks

Estimation of Distribution Algorithms (EDA) are derived from Genetic Algorithms. In an EDA, a probabilistic model is constructed from a population of solutions. The model is then sampled repeatedly to generate a successor population of solutions, thus replacing the traditional use of genetic operators to breed children from selected solutions.

EDAs apply selective pressure by "estimating the distribution" of good solutions. A common approach is to select good solutions from the population and then construct a joint probability distribution of solution variables. Sampling the jpd then generates the next population.

Problem-dependant choices need to be made here about the jpd. Simple (univariate) EDAs assume a fully factorised jpd. More sophisticated approaches assume partial factorisations of the jpd, traditionally classified as bivariate and multivariate EDAs. A common approach is to represent the jpd using a Bayesian Network and an armoury of techniques now exists to build a Bayesian Network from a population of solutions.

In this talk, I will explore an alternative approach to modelling the distribution, which differs from the mainstream in two key aspects. Firstly the jpd is modelled as an undirected graph (Markov Network) and factorised as a Gibbs distribution. Secondly, the construction of the model does not require selection but rather uses solution fitness values directly to determine the parameters of the distribution. The approach has benefits in improving evolution but at some computational expense in building the model. I will demonstrate the costs and benefits of a Markov Network EDA by comparing with other EDAs on a range of problems.

Professor Chris Voudouris, BTexact Technologies, Advanced Planning and Scheduling for Service Operations

The growth of service industries in recent times has been phenomenal with services displacing manufacturing as the main driver of western industrialised economies. In this context, systems for service management are becoming increasingly important for companies to achieve competitive advantage. Two areas of focus are applications for defining/implementing Service Strategy (i.e. design and launch of new services) and also Advanced Planning and Scheduling (APS) solutions for automating and optimising Service Operations.

Advanced Planning and Scheduling has been a well-known term in manufacturing being the core of Supply Chain Management suites. However, the applicability of that part of ERP/SCM systems has been so far limited to product-driven industries. Despite the increased importance of services, integrated APS systems for service operations are yet to emerge from the main enterprise software vendors. More importantly, solutions, where they exist, often address only one industry vertical (e.g. airlines) and/or consider only a part of the people and assets that may exist within an organisation.

In this seminar, I present an APS template for service operations based on technologies for forecasting demand, planning resources, scheduling work and reserving/pricing capacity which links a service enterprise with its customers and suppliers. Artificial Intelligence and Operations Research are playing a major role within the architecture in tackling complex decision making problems. The approach has already been used to structure BT's systems for its extensive field service operations while its applicability to other verticals (e.g. transport, health) is also currently pursued.

Dr Dimitar Kazakov, University of York, Symbolic Learning of Natural Language

Machine Learning (ML) of language studies the acquisition of linguistic knowledge from examples. While statistical learning methods are widely spread nowadays, they have their limitations. The need for adequate modelling of semantics, and acquisition of human-comprehensible theories has led to an increased interest in symbolic learning. The talk demonstrates how Symbolic ML (SML) is used across a range of natural language processing (NLP) tasks. Word morphology learning is chosen to introduce a number of SML techniques and compare supervised and unsupervised learning, followed by a discussion of Inductive Logic Programming (ILP) learning of syntax, lexical semantics and ontologies. Hybrid SML is the last topic covered.

Professor Boris Mirkin, Birkbeck University of London, Tackling Challenges in Clustering: A Data Recovery Approach

Data recovery is a conventional statistics paradigm that has not found much attention in clustering so far, in spite of the fact that such popular algorithms as K-Means partitioning, Ward agglomeration, and CobWeb conceptual clustering can be derived within the data recovery approach. Moreover, this approach gives tools to tackle many issues of current interest in clustering. Examples of such issues are: presence of categorical and quantitative features in data, initial setting in K-Means, interpretation aids for hierarchical clustering, incomplete clustering, multiple data sources. I will introduce the data recovery approach and associated Pythagorean decompositions and show how the issues above can be tackled with these instruments.

Professor Shalom Lappin, Department of Philosophy, King's College, London, Machine Learning and Poverty of Stimulus Arguments in the Theory of Grammar

Since Chomsky (1957) poverty of stimulus arguments have been used to motivate the claim that a powerful domain-specific language faculty is required to account for the facts of first language acquisition. This faculty is regarded as encoding the intricate parameterized principles of a Universal Grammar (UG) that defines the set of possible human languages. Recent work in the application of machine learning methods to grammar induction tasks in natural language processing provides support for an alternative view on which domain general learning algorithms with language models that encode relatively weak language specific assumptions provide the basis for human language acquisition. There are two ways in which this research program can be pursued. In supervised learning the algorithms are trained on data sets annotated with the features to be learned. This method attributes rich representational structure to the data. A more interesting and fruitful approach is to use unsupervised learning in which clustering and distributional patterns are identified in unannotated data in order to extract information on structural dependency relations. To the extent that unsupervised learning with weak bias models succeeds in achieving induction of credible grammars from corpora, it shows that poverty of stimulus arguments for the necessity of UG, construed as a strongly biased language model, do not hold. They demonstrate the viability of an account that takes language acquisition to depend primarily on domain general learning procedures, with minimal assumptions on the nature of linguistic structure that impose weak constraints on the set of possible grammars.

Dr Jonathan Shapiro, University of Manchester, Scaling and sensitivity in machine-learning based optimisation algorithms

Scaling and sensitivity in machine-learning based optimisation Estimation of Distribution Algorithms (EDAs) are optimisation algorithms which use machine learning to learn promising search directions. These algorithms try to learn the relationships between variables in the problem which are necessary to search effectively. In practise, the algorithms have been shown to be effective in certain hard problems.

However, there can also be problems in getting EDAs to work effectively; in many cases the performance is very sensitive to the details of the algorithm. We have shown that the performance of many EDAs on simple problems obeys a scaling relation between the control parameters of the algorithm and the number variables in the problem, and this relationship is problem-dependent. As an example, it has been shown in that EDAs may converge to a state in which the optimum is never found. The probability of never finding the optimum has been shown previously, both in empirical studies and theoretically, to depend on the control parameters and the problem size in a very sensitive and problem dependent way. It is exponential in some problems and polynomial in others. This means that it can be very difficult to set the control parameters of the algorithm in an effective way.

We will reveal the causes of this sensitivity. There are several ways to remove it, which we will present and discuss.

Autumn Term 2005

Professor Philippe De Wilde, A neural basis for uncertainty and its implications for fuzzy logic

Fuzzy logic and Bayesian inference are two popular models of uncertainty. They present a consistent set of results based on mathematical constructions such as membership functions and probability distributions. In this talk we look for neural correlates that can support or suggest improvements to the ways we model uncertainty. We use results from neuro-imaging and neurophysiology that have been established in the last three years. We discuss neuronal population codes, transformations and noise in neural tuning curves, brain areas that are active when we are uncertain, spontaneous activations, and slow noise and other slow processes that affect neural processing. We draw conclusions for fuzzy logic from each of these areas.

Dr Matthew Stone, Generating Animated Utterances

People's utterances in conversation are composed of short, clearly-delimited phrases; in each phrase, gesture and speech go together meaningfully and synchronize at a common point of maximum emphasis. In our work on RUTH, the Rutgers University Talking Head, we rely on this structure to create general-purpose annotation and synthesis to animate an expressive talking face. We can also exploit this structure to create application-specific animated characters using databases of recorded speech and captured motion. Here we start with tools that help create scripts for performers, help annotate and segment performance data, and construct specific messages for characters to use within application contexts. Our animations then reproduce the structure of natural utterances; they recombine motion samples with new speech samples to recreate coherent phrases, and blend segments of speech and motion together phrase-by-phrase into extended utterances. By framing problems for utterance generation and synthesis so that they can draw closely on a talented performance, our techniques support the rapid construction of animated characters with rich and appropriate expression.

Professor Jim Doran, Iruba: an Agent-Based Model of the Guerrilla War Process

It is potentially insightful and useful to model guerrilla wars in agent-based computational terms, as an extension to traditional historical analysis. Accordingly, the Iruba agent-based model of a typical guerrilla war has been designed and implemented. This model will be described, and some experimental results obtained with it will be reported along with the conclusions that may be drawn from them. The results will be interpreted particularly in relation to Guevara and Debray’s theory of “foco” and to a set of sufficient conditions for insurgent success asserted by T. E. Lawrence. Then the limitations of the Iruba and similar models will be considered and the need for and possibility of direct analysis of an agent-based computational model discussed. The advantages and disadvantages of formulating a model as a production system will be considered.

Dr William Harwin, Using Haptics to do Impossible Things

A new way of using computers is emerging based on the technology of the haptic interface. Using these ideas the University of Reading have developed the worlds first virtual drum kit, a haptic torch that could enable blind users to feel without touching, and the first ever virtual building blocks. The technology also has some important medical and therapeutically applications in areas such as stroke rehabilitation. What is this technology, nothing more than a simple robot manipulator, but with a difference. Whereas In the past robots had their own worlds and people discouraged from entering, now the robots are being designed with the specific intension of interacting with people. Now the same technology that builds cars, might soon sit at your desk, at your gym, or in hospitals and could be used by the humans to model cars, manipulate impossible objects and teach us how to play golf like Tiger Woods, or even learn how to use a yo-yo on Mars.

Professor Donia Scott, Applying Natural Language Generation to Electronic Health Records in an e-Science context

At the centre of the Clinical e-Science Framework (CLEF) is a repository of well organised, detailed clinical histories, encoded as data that will be available for use in clinical care and in-silico medical experiments. This talk will present an overview of the CLEF concept, and will describe two ways in which natural language generation technologies are being employed to service the requirements of clinicians and medical researchers as users of CLEF: * a query editing interface, which makes use of natural language generation techniques in order to alleviate some of the problems generally faced by natural language and graphical query interfaces, thereby allowing biomedical researchers and clinicians to query -- in an intuitive way -- the repository of patient data and to receive dynamically generated multimedia responses * a report generator, which produces on-demand a range of medical reports on the clinical histories of individual patients, tailored to different needs (e.g., perspectives and purposes) of the clinician.

Professor Barry Smith, Why Computer Science Needs Philosophy

Human beings are becoming ever increasingly dependent on computers, not only in their everyday traffic with the world but also in their special activities as scientists, doctors, managers, administrators, soldiers. This means that software engineers are increasingly confronted with challenges of a new kind. How can they build software that can do justice in a useful and sophisticated way to the reality of chemistry, or biology, or medicine, or of a large enterprise, or battlefield? How, even more problematically, can they do justice simultaneously to a plurality of such domains - for example of genes/proteins/cells/toxins/diseases - in ways which would allow inferences to be drawn automatically from the corresponding data? One currently popular response to these challenges from the computational side consists in the building of what are called 'ontologies'. We shall examine some of the results of this work, and show in what ways it is confused. We shall then describe how philosophy is being used to resolve such confusions, and indicate more generally the practical value that can be derived from the confrontation between computer science and philosophy.

Dr Goran Nenadic, Mining relationships between biomedical entities from literature

Discovering links and relationships among entities is one of the main challenges in biomedical research, as scientists are interested in uncovering entities that have similar functions, take part in the same/related processes or are co-regulated. This is typically done by mining experimental data. In this talk we will discuss the extraction of semantically related entities from the biomedical literature. The method combines various text-based aspects, such as lexical, syntactic and contextual similarities between entities (identified by domain terms). The results of initial experiments will be presented and compared to other approaches. Several applications that can make use of extracted relationships will be discussed, including the possibilities to combine data from experimental and textual resources.

Professor Trevor Martin, Soft Taxonomies and Semantic Gaps - towards the Intelligent Integration of Information

The combination of information from multiple sources is a widespread problem, mainly because it is rare for two sources to adhere to precisely the same conventions. For example do “author”, “creator” and “composer” label the same property? Even where agreed conventions exist, interpretations may differ – a supplier may take "shippingDate" to be the time when goods are sent, whereas a customer may interpret it as the time when they are delivered. This is an example of a semantic gap – a term which can be interpreted in multiple and possibly inconsistent ways.

The semantic web has a vision is for “islands of standardisation” in which there are small, agreed ontologies for particular sets of users or communities. Wherever two communities meet or overlap, integration of information is a potential problem.

In this seminar I will outline the background to this problem, and argue that it is essential to account for the uncertainty inherent in the process. I will describe a framework for integrating information sources, given a number of approximate mappings between their attributes.

Summer 2005

Debugging Concurrent (Logic) Programs with Abstract Interpretation, Dr Andy King, University of Kent

Some fundamental ideas in computer science just do not seem to quite pan out. One fundamental idea in logic programming is that a logic program consists of a logic component and a control component. The former is a specification in predicate logic whereas the latter defines the order of sub-goals selection. The programmer then defines the control with delay declarations that specify that a sub-goal is to suspend until some condition on its arguments is satisfied -- or at least that is the idea. The problem is that a reasoning about delay declarations is notoriously tricky and it is not unusual for a program and goal to reduce to a state that contains a sub-goal that suspends indefinitely. Suspending sub-goals are usually unintended and often indicate a programming error. This talk will show how these programming errors can be located by abstract interpretation. A debugger/analyser/verifier will be demonstrated which has been used to locate concurrency bugs in real programs.

Experimenting with a real-size man-hill to optimise pedagogical paths, Mr George Valigiani, Universite du Littoral Cote d'Opale (ULCO)

This seminar will describe experiments aimed at adapting Ant Colony Optimisation (ACO) techniques to an e-learning environment, thanks to the fact that the available online material can be organised in a graph by means of hyperlinks between educational topics. The structure of this graph is to be optimised in order to facilitate the learning process for students. ACO is based on an ant-hill metaphor. In this case, however, the agents that move on the graph are students who unconsciously leave pheromones in the environment. The whole process is therefore referred to as a "man-hill" Real-size tests have been performed, showing that man-hills behave differently from ant-hills.

Evolvable Intelligent Systems, Dr Plamen Angelov, Lancaster University

Currently existing systems that use artificial intelligence rely on fixed rule-bases or neural networks. They are trained off-line and do not adapt to the environment. They do not develop their structure (do not evolve). The environment, in which real systems operate, however, is often unpredictably changing. This applies to robotic systems, transport vehicles, technological processes, etc. The challenge is to develop systems capable of higher level adaptation to the environment in which they operate and to internal changes.

Evolving systems aim to address this challenge. They should be able to change their structure in addition to their parameters which conventional adaptive system does. They should be able to grow, update and shrink when necessary. Having this ability they will be capable of adapting their goals, strategy, and constraints while performing a task. This concept is closely related to the recent advances in real-time evolving rule-based and neuro-fuzzy systems. It lies on the crossroads of computational intelligence and system theory and borrows heavily from the biological and life sciences. Thus the field is highly interdisciplinary and its applications - highly innovative.

The talk will concentrate to the problems and results the author encountered during last several years of research in this emerging area as well as to one particular approach to on-line identification of Takagi-Sugeno fuzzy models and some engineering applications.

A Study of Neo-Austrian Economics using an Artificial Stock Market, Harald A. Benink, Rotterdam School of Management, Erasmus University, Netherlands; Christopher R. Stephens, Dept. of Computer Science, University of Essex, UK and Instituto de Ciencias Nucleares, UNAM, Mexico

An agent-based artificial financial market (AFM) is used to study market efficiency and learning in the context of the Neo-Austrian economic paradigm. Efficiency is defined in terms of the 'excess' profits associated with different trading strategies, where excess for an active trading strategy is defined relative to a dynamic buy and hold benchmark. We define an Inefficiency matrix that takes into account the difference in excess profits of one trading strategy versus another ('signal') relative to the standard error of those profits ('noise') and use this statistical measure to gauge the degree of market efficiency. A one-parameter family of trading strategies is considered, the value of the parameter measuring the relative 'informational' advantage of one strategy versus another. Efficiency is then investigated in terms of the composition of the market defined in terms of the relative proportions of traders using a particular strategy and the parameter values associated with the strategies. We show that markets are more efficient when informational advantages are small (small signal) and when there are many coexisting signals. Learning is introduced by considering 'copycat' traders that learn the relative values of the different strategies in the market and copy the most successful one. We show how such learning leads to a more informationally efficient market but can also lead to a less efficient market as measured in terms of excess profits. It is also shown how the presence of exogeneous information shocks that change trader expectations increases efficiency and complicates the inference problem of copycats.

Fast Monte Carlo methods when asset returns are Levy processes, Dr Nick Webber, University of Warwick

Simulation methods are frequently used to value financial instruments whose payoffs depend upon a sequence of stock prices or FX rates. Fast simulation methods are essential to enable traders to quote prices in real time.

A standard assumption is that asset return processes are scaled arithmetic Brownian motions. In this situation Brownian bridge methods can be used as an effective variance reduction technique.

Empirically, asset return processes are not Gaussian. Models based on processes such as the Variance-Gamma (VG) and Normal Inverse Gaussian (NIG) Levy processes have recently been developed. In this talk we develop bridge methods that can be used with these processes. We demonstrate that considerable variance reduction can be achieved for a range of path-dependent financial instruments.

Spring Term 2005

Satellite Positioning in the 21st Century, Dr David Park, University of Nottingham

The American owned Global Positioning System (GPS) is still the only fully operational satellite navigation system in the world. However, in the coming 5 to 10 years, a number of additional global, regional and local positioning and navigation systems will be developed and deployed, including Europe’s Galileo system.

This talk will provide a quick introduction to the field of satellite positioning including its history, how it works, and current commercial, scientific and military applications. A closer look at some of the new satellite-based systems likely to be available in the next 10 years, including Galileo, GPS III and EGNOS, will then be followed by a brief look at the various terrestrial/on-board positioning sensors that are likely to be integrated into every ‘Satnav’ system within the next 10 years (eg inertial navigation systems).

It is hoped that an open question and answer session will be possible at the end of the presentation where any outstanding issues can be discussed further.

Adaptive Trader Agents and Automated Design of Auction Markets, Dr Dave Cliff, HP Europe

No abstract available

Embodied Communication, Professor Ipke Wachsmuth, Universitaet Bielefeld

Face-to-face communication cannot be reduced to the exchange of verbal information. While speaking, one can e.g. indicate the size and shape of an object by a few handstrokes, direct attention to an object by pointing or gaze, and modify what is being said by emotional facial expression. The meanings transmitted in this way are multimodally encoded and strongly situated in the present context and especially the body.

With the artificial humanoid agent MAX under development at Bielefeld University we investigate embodied communication by way of computer simulations in virtual reality. Equipped with a synthetic voice and an animated body and face, Max is able to speak and gesture, and to mimic emotions. By means of microphones and tracker systems, Max can also "hear" and "see" and is able to process spoken instructions and gestures. Current research challenges include what is required for enabling an artificial agent to imitate human gestures, and how it could express event-related emotions.

In a multidisciplinary endeavour, embodied communication (in humans and machines) is also the theme of a forthcoming ZiF research year, which will bring together theoreticians, empirical and applied researchers from the cognitive, neuro- and computer sciences. The aim is to develop an integrated perspective of embodiment in communication and study novel forms of human machine communication.

Addressing the Knowledge-Acquisition Bottleneck for Deep, Wide-Coverage, Probabilistic, Constraint-Based Grammar Development, Professor Josef van Genabith, Dublin City University

"Deep" grammars relate strings to information/meaning, usually in the form of predicate-argument structures, deep dependency relations of logical forms. "Shallow" grammars define languages as sets of strings and may associate syntactic structures (e.g. CFG parse trees) with strings. Natural languages do not always interpret linguistic material locally where the material is encountered in the string (tree etc.). In order to obtain accurate and complete information/meaning representations, a hallmark of deep grammars is that they usually involve a long-distance dependency (LDD) resolution mechanism.

Traditionally, deep grammars are hand-crafted. Wide-coverage, deep grammar development, particularly in rich constraint-based formalisms such as LFG and HPSG, is knowledge-intensive, time-consuming and expensive, constituting an instance of the (in-) famous "knowledge acquisition bottleneck" familiar from other areas in traditional, rule-based AI and NLP and very few deep grammars (Riezler et al., 2002) have, in fact, been successfully scaled to unrestricted input.

The last 15 years have seen extensive efforts on treebank-based automatic grammar acquistion using a variety of machine-learning techniques (e.g. Charniak, 1995; Collins, 1997; Johnson, 1998; Charniak, 2000; Johnson, 2002). The grammars induced are wide-coverage and robust and, in contrast to manual grammar development, machine-learning-based grammar induction incurrs relatively low development cost. With few notable exceptions (Collins' Model 3, Johnson, 2002), however, these treebank-induced wide-coverage grammars are shallow: they usually do not attempt to resolve long-distance dependencies nor associate strings with information/meaning representations (in the form of predicate-argument structure, dependency relations or logical forms).

In this talk I present joint research (Cahill et al. 2004) on automatic, treebank-based induction of, and processing with, wide-coverage deep probabilistic LFG resources (grammars and lexica) based on automatic f-structure annotation algorithms (for English - Penn-II, German -TIGER and Chinese -Penn Chinese treebanks). I'll compare our approach with recent treebank-based work in Combinatory Categorical Grammar (Hockenmaier and Steedman, 2002) and HPSG (Miyao and Tsujii, 2004).

Long-Distance Dependency Resolution in Automatically Acquired Wide-Coverage PCFG-Based LFG Approximations A. Cahill, M. Burke, R. O'Donovan, J. van Genabith, and A. Way. In Proceedings of the 42nd Annual Conference of the Association for Computational Linguistics (ACL-04), July 21-26, 2004, Barcelona, Spain, pp.320-327

Constructing Semantic Space Models for Parsed Corpora, Dr Mirella Lapata, Sheffield University

Traditional vector-based models use word co-occurrence counts from large corpora to represent lexical meaning. In this talk we present a novel framework for constructing semantic spaces that takes syntactic relations into account. We introduce a formalisation for this class of models which allows linguistic knowledge to guide the construction of semantic spaces. We evaluate our framework on tasks that are relevant for cognitive science and NLP: semantic priming, lexicon acquisition and word sense disambiguation. In all cases we show that our framework obtains results that are comparable or superior to state-of-the art.

Web Intelligence, Professor Nigel Shadbolt, University of Southampton

The extraordinary human construct that is the World Wide Web is a truly Disruptive Technology. There are now hundreds of millions of users, billions of indexed web resources, it is used in every country on Earth and yet only a tiny percentage of users is “trained” in any way. This remarkable construct is both massively distributed and largely open.

With this amount of content and usage the integration of information across space and time leads to new opportunities. From on-line shopping to collaborative e-Science the web is changing how information is generated, deployed and used. The military will have few options but to take advantage of the huge investment that the commercial and research sectors will make in web service solutions and architectures.

This lecture will examine the extent to which intelligent web services are evolving to cope with diverse sources of information on a global scale. It will examine the particular way in which Artificial Intelligence is being woven into the web. It will review how these developments are likely to shape future military capabilities.

On Concepts, Words and Syntax: the featural and unitaryspace semantic hypothesis and beyond, Dr Gabriella Vigliocco, University of Sheffield

No abstract available

The Power of Large-Scale Multi-Agent Systems, Dr George Rzevski, Multi-Agent & Distributed Intelligence Research Alliance, Brunel University

Four years ago Professor Rzevski has founded a software development company to commercialise his research into Ontology-based Multi-Agent Systems. The company is rapidly expanding and currently has 100 employees producing large-scale multi-agent software for diverse applications such as transportation logistics, design of very large structures, data clustering and text understanding. The key ideas will be illustrated by examples of large-scale commercial systems.

Appearance-based Methods in Mobile Robot Navigation, Dr Fred Labrosse, University of Wales

In this seminar, I will address the problem of using vision for tasks related to mobile robot navigation (in particular mapping and homing). I will claim that the only method worth using is based only on the appearance of the environment (and will give the audience the opportunity to contest this claim ;-), will discuss the implications and limitations of this claim and will show how this can be done. I'll show results for different tasks and talk about where we are going with this.

Autumn 2004

Blind Source Separation in EM Brain Signals, Dr Christopher J. James, Centre for Biomedical Signal Processing and Control, ISVR University of Southampton

Blind Source Separation (BSS) and Independent Component Analysis (ICA) are increasing in popularity in the field of biomedical signal processing. They are generally used when it is required to separate measured multi-channel biomedical signals into their constituent underlying components. The use of ICA has been facilitated in part by the free availability of toolboxes that implement popular flavours of the techniques. Fundamentally ICA in biomedicine involves the extraction and separation of statistically independent sources underlying multiple measurements of biomedical signals. Enhanced results are obtained with the introduction of ICA/BSS techniques based on time, frequency and joint time-frequency decomposition of the data.

This talk will focus on my work using such BSS techniques applied to the analysis of electromagnetic (EM) brain signals - i.e. the electroencephalogram and the magnetoencephalogram. These techniques are used for decomposing measured EM brain signals into their varied underlying sources - these have multiple uses, from predicting the onset of an epileptic seizure, to monitoring changing brain-state in a neurological Intensive Care Unit.

Agent-environment State Machines, Dr Tom Ziemke, University of Skövde, Sweden

Traditional cognitive science and AI viewed cognition as, roughly speaking, the computational manipulation of internal representational knowledge of the external world through state machines (e.g., of the Turing machine type). Most of this is strongly questioned in recent work on embodied, situated and distributed cognition. However, using examples from robotic experiments and human case studies, this talk will illustrate that the notion of state machines is also useful in describing distributed cognitive processes emerging from the interaction of embodied agents and their environments, where knowledge and states are not purely internal, but distributed over agent and environment.

Aerobots (Flying Robots) for Future Planetary Exploration, Dave Barnes, University of Wales, Aberystwyth

Aerobot technology is generating a good deal of interest in planetary exploration circles. Balloon based aerobots have much to offer ESA's Aurora programme, e.g. high resolution mapping, landing site selection, rover guidance, data relay, sample site selection, payload delivery, and atmospheric measurement. Aerobots could be used in a variety of configurations from uncontrolled free-flying to tethered rover operation, and are able to perform a range of important tasks that other exploration vehicles cannot. In many ways they provide a missing ‘piece’ of the exploration ‘jigsaw’, acting as a bridge between the capabilities of in-situ rovers or landers and non-contact orbiters. Technically, a Lighter then Air (LTA) aerobot concept is attractive because it is low risk, low-cost, efficient, and much less complex than Heavier than Air (HTA) vehicles such as fixed wing gliders, and crucially, much of the required technology ‘building blocks’ currently exist. Smart imaging and localisation is a key enabling technology for remote aerobots.

The University of Wales Aberystwyth (UWA) has been working in the area of aerobots for future planetary exploration for a number of years, and is involved in an ESA funded contract to develop a demonstrator imaging and localisation package (ILP) for a Martian balloon. This ILP system will incorporate a unique combination of image based relative and absolute localisation techniques. Additionally the Aberystwyth team is working on a possible tethered balloon system for the ESA Aurora ExoMars rover study.

This talk provides a background to the application of aerobot technology for future planetary exploration, and details the research being undertaken at Aberystwyth in the areas of aerobot imaging and localisation, and tethered balloon/rover operations.

Satellite Positioning in the 21st Century, Dr David Park, IESSG, University of Nottingham

The American owned Global Positioning System (GPS) is still the only fully operational satellite navigation system in the world. However, in the coming 5 to 10 years, a number of additional global, regional and local positioning and navigation systems will be developed and deployed, including Europe’s Galileo system.

This talk will provide a quick introduction to the field of satellite positioning including its history, how it works, and current commercial, scientific and military applications. A closer look at some of the new satellite-based systems likely to be available in the next 10 years, including Galileo, GPS III and EGNOS, will then be followed by a brief look at the various terrestrial/on-board positioning sensors that are likely to be integrated into every ‘Satnav’ system within the next 10 years (eg inertial navigation systems).

It is hoped that an open question and answer session will be possible at the end of the presentation where any outstanding issues can be discussed further.

Checkers: Assessing the limits of evolution, Dr Evan Hughes, Cranfield University

Computer checkers players that have been designed through evolution are now available commercially. This talk details a study into whether classic playing strategies can really be evolved from nothing, or whether many of the evolved players seen in the literature actually benefit from expert knowledge. The results suggest that some common heuristics will evolve, but it is not necessarily easy. The implications for other evolved players in the literature are analysed and discussed.

Summer 2004

Investigations into Graceful Degradation of Evolutionary Developmental Software, Dr Peter Bentley, University College London

Today’s software is brittle. A tiny corruption in an executable will normally result in terminal failure of that program. But nature does not seem to suffer from the same problems. A multicellular organism, its genes evolved and developed, shows graceful degradation: should it be damaged, it is designed to continue to work. This talk describes an investigation into software with the same properties, focussing on the use of fractal proteins in a model of evolutionary development.

Three programs, one human-designed, one evolved using genetic programming, and one evolved and developed using the fractal developmental system are compared. All three calculate the square root of a number. The programs are damaged by corrupting their compiled executable code, and the ability for each of them to survive such damage is assessed. Experiments demonstrate that only the evolutionary developmental code shows graceful degradation after damage.

Learning Units, Dr Remagnino and Dr Ndwdi Monekosso, Kingston University 

Reality is too complex and unpredictable; models cannot be precompiled to cope with real data. The talk will present an ongoing endeavour to design algorithms to deal with real data in complex environments. Examples will be given in computer vision and computer animation. Learning can be used in computer vision to identify events of interest, calibrate cameras and combine information in a multi-view system. Learning can be employed in computer animation to build self-sustainable avatars, able to optimise their actions in a complex search space. Learning can be devised in isolation, but, most importantly, it can be distributed across a number of modules that, when communicating, synergistically give birth to emerging functionality. 

Lightweight Formal Tool Support for Software Evolution, Tom Mens, University of Mons-Hainaut

Since all software is subject to evolution, there is a clear need for better tools to support and automate various aspects of software evolution. Such tools are not only needed at the level of source code (evolution of programs) but also at a design level (evolution of models).

During my talk I will briefly sketch two formal approaches that can be used to provide better support for software evolution. First, I will present an approach for software refactoring that relies on the formalism of graph transformation. Second, I will present an approach for maintaining the consistency of evolving UML models based on the formalism of description logics. 

Parser Combinators for Natural Language Parsing, Professor Jan van Eijck, CWI, Netherlands

Parser combinators are higher order functions that transform parsers into parsers. Parsing with context free grammars can be handled by defining combinators for the key operations in context free grammar rules: recognizing epsilon, recognizing a terminal, choice between rewrite rules for a given nonterminal, and sequencing under a nonterminal. After explaining and illustrating this in some detail, we demonstrate how (stack) parser combinators can be used for the analysis of movement (or extraction) in natural language.

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Spring 2004

Support Vector Machines for Chain-coded Hand-written Digit Recognition, Professor Chih-Jin Lin, National Taiwan University

Support vector machines are not directly suitable for data with different length. Chain-coded hand-written digit recognition is a problem of this type. In this talk I will discuss several ways of using SVM on such data sets. In particular, we show that edit distance between two sequences can be good features (attributes) for transforming original data to a form suitable for SVM. Good accuracy on some real-world data sets will be presented. Furthermore, I will discuss some implementation issues.

Modelling and Reasoning about Extended Business Transactions, Professor Michael Butler, University Of Southampton

In this talk I will give an overview of a formal modelling language called StAC (Structured Activity Compensation) which resulted from a collaboration with a group at IBM Hursley. Compensation is an important feature of StAC. This is a generalisation of the traditional commit/rollback approach in traditional transaction processing designed to deal with situations where an undo is infeasible in the case of error. I will also talk about the relationship between StAC and the BPEL4WS business coordination language for web services.

Multi-modal Representations for Visual Scene Analysis, Dr Norbert Krueger, University of Stirling

Vision faces the problem of an extremely high degree of vagueness and uncertainty in its low level processes such as edge detection, optic flow analysis and stereo estimation. However, the human visual systems acquires visual representations which allow for actions with high precision and certainty within the 3D world under rather uncontrolled conditions. Humans can achieve the needed certainty and completeness by integrating visual information across visual and sensorial modalities. 

This integration is manifested in the huge connectivity between brain areas in which the different visual modalities are processed as well as in the large number of feedback connections between higher and lower cortical areas. The essential need for integrating information across modalities in addition to optimising single modalities has been recognised in the vision community after a long period of work on improving single modalities. The power of modality fusion arises from the intrinsic relations given by deterministic and statistic regularities across visual modalities and across different sensors (e.g., haptic information and vision). Two important regularities in visual data with distinct properties are (1) motion (most importantly rigid body motion, RBM) and (2) statistical interdependencies between features such as collinearity and symmetry. 

In my talk I will introduce a multi-modal representation that has been applied to the problem of visual scene analysis. This representation codes information in terms of local visual Primitives in a sparse and condensed way. The Primitives code orientation, grey value, colour, optic flow, stereo information as well as information about "junction-ness" or "edge-ness". They are motivated by hyper-column structures in the visual cortex. The Primitives initialise a disambiguation process that utilizes statistical and deterministic regularities. Besides its usefulness for computer vision our-multi modal representation have resulted in a new form of computer art, called `symbolic pointillism'. Finally, a haptic exploration of objects will be shown and its potential interaction with vision will be discussed.

Generation of Multiple-choice Tests, Professor Ruslan Mitkov, University of Wolverhampton

The objective of this on-going project is to deliver a comprehensive computer-aided environment for generating multiple-choice tests from electronic instructional documents (textbooks). In addition to employing various Natural Language Processing (NLP) techniques including among many other things, term extraction and semantic distance computing, the system makes use of language resources such as corpora and ontologies. It identifies sentences containing important concepts and generates test questions as well as distractors, offering the user the option to post-edit the test items in a user-friendly interface. The talk will present the evaluation results of the current system which show that in assisting test developers to produce tests in a significantly faster, expedient manner and without any compromise of quality, this new tool saves both time and production costs.

Biologically Inspired Mechanisms for Robot Learning through Imitation, Dr Yiannis Demiris, Imperial College London

An introduction to Stochastic Diffusion Processes, Dr Mark Bishop, Goldsmiths College

This introductory talk is tailored for people who haven't come across SDPs before - it begins with a novel example to illustrate principles behind SDPs called, 'The Restaurant Game'; gives a taxonomy of the key types of SDS, (together with examples of SDPs in nature), discusses properties of the algorithm and concludes with a demo of various different SDPs in action performing 'best-fit' string search.

CSP and Communicating B Machines, Professor Steve Schneider, University of London

Recent work on combining CSP and B has provided ways of describing systems comprised of components described in both B (to express requirements on state) and CSP (to express interactive and controller behaviour). This approach is driven by the desire to exploit existing tool support for both CSP and B, and by the need for compositional proof techniques. This talk is concerned with the theory underpinning the approach, and discusses a number of results for the development and verification of systems described using a combination of CSP and B, with particular focus on concurrency and abstraction. The talk discusses how results obtained (possibly with tools) on the CSP part of the description can be lifted to the combination. The results are illustrated with a toy lift controller running example.

A Predicative Model for General Correctness, Dr Steve Dunne, University of Teesside

The predicative approach to program semantics was pioneered by Hehner, and adopted by Hoare and He for their seminal work on unifying theories of programming. I will formulate alternative healthiness conditions intended to characterise predicates which give us a general-correctness rather than total-correctness description of sequential program behaviour. Surprisingly, these are simpler and have more mathematically desirable properties than Hoare and He's original ones, suggesting that general correctness might be a more suitable basis for sequential program semantics than total correctness.

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Autumn 2003

Thesauruses for Natural Language Processing, Adam Kilgarriff, ITRI Brighton

The range of roles for thesauruses within NLP is presented and the WASPS thesaurus is introduced.  We argue that manual and automatic thesauruses are alternative resources for the same NLP tasks. This involves the radical step of interpreting manual thesauruses as classifications of words rather than word senses: the case for this is made. Thesaurus evaluation is now becoming urgent. A range of evaluation strategies, all embedded within NLP tasks, is proposed.

Robust Hybrid Designs For Real-Time Simulation Trials, Professor Russell Cheng, University Of Southampton

Real time simulation trials involve people and are particularly subject to a number of natural constraints imposed by standard work patterns as well as to the vagaries of the availability of individuals and unscheduled upsets. They also typically involve many factors. Well thought-out simulation experimental design is therefore especially important if the resulting overall trial is to be efficient and robust. We propose hybrid experimental designs that combine the safety of matched runs with the efficiency of fractional factorial designs. This article describes real experiences in this area and the resulting approach and methodology that has evolved from these and which has proved effective in practice.

Evolving Developmental Programs For Emergent Computation, Dr Julian Miller, University Of Birmingham

The mechanisms whereby mature multicellular biological organisms are constructed from the original fertilized egg are extraordinary and complex. Living organisms are perpetually changing yet outwardly they often give the appearance of stasis. This is accomplished by cells constantly reproducing and dying. There is no central control behind this. Such a process leads naturally to the self-repairing properties of organisms. At present humans design systems using a top down design process. Until now, this methodology of design has served humanity well. However when one looks at the problem of constructing software and hardware systems with of the order of 1013 or more components (roughly the number of cells in the human body) it appears that we face a design crisis. As the number of interacting components grows the complexity of the system grows also. This can lead to many problems. It becomes difficult to formally verify such systems due to the combinatorial explosion of configurations or states. The cost of maintenance grows alarmingly also and their unreliability increases. Electronic circuits are now routinely constructed at sub-micron level. This is leading to immense wiring problems. As the trend for increasing miniaturization of circuits continues, it will become ever more difficult to supply the huge amounts of data required to define or program these devices. Living systems demonstrate very clever solutions to these problems. The information defining the organism is contained within each, and every, part. There is no central design. Consequently, it is likely that designers will have to increasingly turn to methods of constructing systems that mimic the developmental process that occur in living systems. To some extent, such an agenda has been embraced in the nascent field of amorphous computing.

I will describe my recent work on evolving developmental programs inside a cell to create multicellular organisms of arbitrary size and characteristics. The cell genotype is evolved so that the organism will organize itself into well defined patterns of differentiated cell types (e.g. the French Flag). In addition the cell genotypes are evolved to respond appropriately to environmental signals that cause metamorphosis of the whole organism. A number of experiments are described that show that the organisms exhibit emergent properties of self-repair and adaptation.

Lucy and the Quest for the Holy Grail, Steve Grand, Cyber Life Limited

Lucy and the quest for the holy grail. The cerebral cortex seems to be a general-purpose machine with the capacity to self-organise into an assortment of useful specialist machines, guided only (or at least mostly) by the information being supplied to it. This is such a striking facility that one might expect models of cortical function to lie at the heart of most AI research, yet in practice it seems to receive very little attention. There are several good reasons for this, but it doesn't follow that the problem is intractable. Lucy the robot is my (amateur and largely unfunded) attempt to discover the fundamental engineering principles that lie behind cortical organisation. The project is still in its early days, but nevertheless is starting to show promise. This is a progress report. 

Agent-Based Models of Financial Markets, Professor Chris Stephens, Institute for Nuclear Sciences of the UNAM

A pedagogical introduction to financial markets, both real and virtual, will be given, showing how one can model a financial market using "intelligent" artificial agents. I will consider elements such as: organizational structure of the market, models of various types of market participant, such as market makers, price formation and information and learning. In particular I will consider the phenomenon of adaptation and learning, showing how it is, essentially, a problem in signal identification in a noisy environment. Implications for the efficiency of markets will be considered.

Neuro Robots: Issues in Evolvability and Plasticity, Professor Phil Husbands, University of Sussex

Recent years have seen the discovery of freely diffusing gaseous neurotransmitters, such as nitric oxide (NO), in biological nervous systems. A type of artificial neural network (ANN) inspired by such gaseous signalling, the GasNet, has previously been shown to be more evolvable than traditional ANNs when used as artificial nervous system in an evolutionary robotics setting. Evolvability is used in the sense of consistent speed to very good solutions, here appropriate sensorimotor behaviour generating systems. We present two new versions of the GasNet which take further inspiration from the properties of neuronal gaseous signalling. The plexus model is inspired by the extraordinary NO producing cortical plexus structure of neural fibres and the properties of the diffusing NO signal it generates. The receptor model is inspired by the mediating action of neurotransmitter receptors. Both models are shown to significantly further improve evolvability. We describe a series of analyses suggesting that the reasons for the increase in evolvability is related to the flexible loose coupling of distinct signalling mechanisms, one `chemical' and one `electrical'.

Learning Domain Theories, Professor Steve Pulman, Oxford University

By `domain theories' we mean collections of non-linguistic facts and generalisations or `commonsense theories' about some domain. Traditional approaches to many problems in AI, and in NL disambiguation, presupposed that such theories were to hand for the relevant domain of application. Notoriously these were difficult to formalise and fragile to use, and over recent years the traditional approach has been almost entirely superseded by statistical learning mechanisms. However, this does not mean that the traditional view was entirely wrong: it is clear that many aspects of linguistic interpretation are based on reasoning to novel conclusions of a type that is unlikely to be replicable by purely statistical methods. This talk reports some recent experiments on learning domain theories automatically, starting with parsed corpora, and using various machine learning techniques, particularly Inductive Logic Programming, to derive some simple Domain Theories from them.

Computational Semantics for Spoken Dialogue Systems, Dr Johan Bos, Oxford University

If we want dialogue systems to cope with the semantic content of utterances, we need tools that build meaning representations, detect inconsistencies, distinguish old from new information, extract specific features, and determine speech acts (and possibly more). Moreover, all of this needs to be accomplished in a systematic and efficient way, taking situational and background knowledge into account. In this talk I investigate how realistic this goal is by illustrating the role of computational semantics and inference in spoken dialogue systems in general, and for two tasks in particular:

(1) using linguistically motivated grammars to construct semantic representations from spoken input;

(2) using methods from automated deduction (in particular theorem proving and model building) to interpret natural language instructions.

In the last part of the talk I will discuss two applications of spoken dialogue systems (a mobile robot and home automation) that successfully use these techniques, showing that we are significantly closer to our goal than even a few years ago. Finally, I will suggest future research directions and challenges for the deployment of computational semantics in dialogue systems with larger domains.

Graph Colouring as a Model of Parallel Computation, Dr Alexei Vernitski, University of Essex

In graph theory, the problem of colouring a graph is the problem of assigning a colour (from a restricted list of available colours) to each vertex of the graph in such a way that no two connected vertices are assigned the same colour. We can imagine solving a graph colouring problem in a parallel architecture: let each vertex of the graph have a simple processor, whose sole function is to assign a colour to the vertex, making sure that this is not the same colour as of any of its adjacent vertices. Unexpectedly, this architecture has an excellent computational power: I shall show how it can calculate every Boolean function, and, moreover, every function of multiple-valued logic.

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Summer 2003

An Introduction to Drift Analysis for Estimating Computation Time of Evolutionary Algorithms, Dr Jun He and Xin Yao, University of Birmingham

Evolutionary algorithms (EAs) have been used widely in solving combinatorial optimisation problems. However, few theoretical results exist that analyse the time complexity of EAs for these problems. Recently, this topic has being attracted a lot of interests from researchers both in theoretical computer science and in evolutionary computation. This talk will report the application of drift analysis in estimating computation time of EAs. Drift analysis aims to derive some important properties of a Markov chain (or a general stochastic process), e.g. the expected first hitting time to the optimum, by its one-step mean drift. Based on drift analysis, we will present some useful drift conditions for analysing the time complexity of EAs.

Machine Scheduling: Complexity, Algorithms and Approximability, Dr Bo Chen, University of Warwick

This talk will provide a brief introduction to a challenging research area in scheduling by focusing on the algorithmic aspects of scheduling problems. 

Simulations for failure semantics, Eerke Boiten, University College London

Data refinement is a generally accepted and well established method of refining sequential programs. Data refinement is normally verified using so-called simulations, which provide a sound and complete method.

This talk demonstrates how the simulations method can also be applied to concurrent systems. In concurrent systems, notions of refinement correspond to notions of "testing" of such systems, which vary according to what can be observed in a "test". Such observations can be added explicitly to those already made by sequential programs, yielding a new semantics. In particular, when these observations correspond to sets of refused operations, we obtain a failures semantics. The established data refinement theory then provides a notion of simulation, which can be used to verify failures refinement.

Improving the performance of support vector methods, Prof Tom Downs, School of Computer Science and Electrical Engineering, University of Queensland

Abstract classification and for regression. It will also describe some variations on the SVM theme that have been developed in recent years to provide either simpler learning algorithms or improved generalization performance. Some examples of the application of SVMs to practical datasets will be given with performance compared to other leading techniques.

Finally the seminar will describe some recent work carried out at the University of Queensland which allows the exact simplification of the (sometimes quite complex) solutions provided by SVMs. An additional method, that has grown out of this technique, will be described that allows identification of redundant training data (ie data that contribute nothing to the learning process beyond what is provided by other data).

Search-Based Software Testing, Mark Harman, Brunel University

Search-based testing is the name given to a set of problems where test data is to be generated using search-based algorithms, most notably genetic algorithms. There is a growing interest in search-based testing techniques, reflecting a similar increase in awareness of search-basedtechniques for solving a wide class of traditional problems in software engineering.

The search for good quality test data is a central plank of many organisations' verification and validation strategies. It turns out that many of the criteria that define "good quality" in test data form ideal candidates for fitness functions. In this talk I will present a survey of the field, relating both academic results and industrial practice, focusing in particular, on my joint work with DaimlerChrysler's Evolutionary Testing group. (This work is also conducted in collaboration with Chris Fox of Essex University.)

Navigation of the Titan 4WD outdoor mobile robot using a custom made phase array CTFM sonar for landmarks recognition, Dr Danny Ratner, Aerial and Mobile Robotics Research Group, University of Wollongong

The talk will present ongoing research at the Aerial and Mobile Robotics Research Group at the University of Wollongong, focussing on the continuous transmission frequency modulated sonar sensors developed at Wollongong. These sensors can be used for range finding as well as identifying objects by the spectrum of their sonar response. The talk will also cover aspects of the work carried out the a the Israeli Air Force Laboratory (Rafael) and through the DDARM2 project at CMU with Takeo Kanade and others).

Co-operative Intelligent Systems, Prof. Mohamed Kamel, Dept. Of Systems Design Eng, University of Waterloo, Canada

Research on cooperative, adaptive intelligent systems, involves studying, developing and evaluating architectures and methods to solve complex problems using adaptive and cooperative systems. These systems may range from simple software modules (such as a clustering or a classification algorithm) to physical systems (such as autonomous robots or sensors). The main characteristic of these systems is that they are adaptive and cooperative. By adaptive, it is meant that the systems have a learning ability that makes them adjust their behaviour or performance to cope with changing situations. The systems are willing to cooperate together to solve complex problems or to achieve common goals.

In this talk we will report on the recent research in the Pattern Analysis and Machine Intelligence group on Cooperative Intelligent Systems. We will focus on adaptive aggregation methods and architectures, use of agents and multilearning with applications in classification, sensor planning and robotics.

Optimization in WDM Optical Networking: Some New Challenges, Dr. Gaoxi Xiao, School of EEE, Nanyang Tech. University of Singapore

This talk is composed of two parts. The first part will be a very brief introduction to the Network Technology Research Centre (NTRC) of Nanyang Technological University (NTU), where the speaker is conducting most of his research jobs.

In the second part, the speaker will provide a personal view of some new challenges in WDM optical networking. Specifically, he would discuss several problems that the optimization specialists have incurred; this information probably will be very beneficial.

A Practical Guide to Support Vector Classification, Chih-Jen Lin, National Taiwan University

Support vector machine (SVM) is a popular technique for classification. However, people who are not familiar with SVM often get unsatisfactory results since they may miss some simple but significant steps. In this talk, we first discuss a simple procedure, which usually gives reasonable results. It has been fully implemented in our package libsvm, one of the main SVM software. Secondly, we discuss challenging cases for which this simple procedure may not perform well and then some possible research issues.

Genetic Programming mining DNA chip data, W. B. Langdon, GlaxoSmithKline

Some initial experiments using GP to evolve comprehensible robust predictive models based on gene expression as measured by DNA chips will be described. DNA chip data is notoriously difficult to data mine. It is noisy, has thousands of features but often few training examples. 

The approach is based on selecting genes via a small number of iterations of many GP runs in parallel. In a final iteration a non-linear model is evolved using a few tens of genes. Bloat is combated by size fair crossover.

Simple models using few genes are preferred, as these may be of interest to Biologists, over complex models using more genes (even though these bigger models may have greater accuracy when measured on the limited data available).

Comparison of data and process refinement, Dr David Streader, University of Waikato, New Zealand

From what point of view is it reasonable, or possible, to refine a one place buffer into a two place buffer? In order to answer this question we characterise refinement based on substitution in restricted contexts. We see that data refinement (specifically in Z) and process refinement give differing answers to the original question, and we compare the precise circumstances which give rise to this difference by translating programs and processes into labelled transition systems, so providing a common basis upon which to make the comparison. We also look at the closely related area of sub-typing of objects. Along the way we see how all these sorts of computational construct are related as far as refinement is concerned, and discover and characterise some (as far as we can tell) new sorts of refinement.

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Spring 2003

Pattern Analysis in Practice, Prof. Ian Nabney, Aston University

The aim of this talk is to demonstrate that the key to success in developing pattern analysis applications is to achieve a principled pragmatism; flexibility without prejudice.

We need theory to provide a wide range of models (with different motivating assumptions) and so that we can apply appropriate techniques given the structure of a problem. However, an application is not just about picking a model with the best accuracy: practical questions are not settled completely by minimising a sum-of-squares error function. In particular, a model forms part of a larger system that must be accessible to users which leads to a broader range of challenges.

The main differences from purely theoretical investigations will be described and illustrated with real examples including wind field modelling, oil drilling condition monitoring, ECG arrhythmia analysis, bioinformatics and medical diagnosis

GOAL: A Goal Oriented Agent Language, Prof Wiebe van der Hoek, Liverpool University 

A long and lasting problem in agent research has been to close the gap between agent logics and agent programming frameworks. The main reason for this problem of establishing a link is identified and explained by the fact that agent programming frameworks have not incorporated the concept of a declarative goal. Instead, such frameworks have focused mainly on plans or goals-to-do instead of the end goals to be realised which are also called {\em goals-to-be}. In this talk, a new programming language called GOAL is introduced which incorporates such declarative goals. The notion of a commitment strategy - one of the main theoretical insights due to agent logics, which explains the relation between beliefs and goals - is used to construct a computational semantics for GOAL. Finally, a proof theory for proving properties of GOAL agents is introduced. Thus, we offer a complete theory of agent programming in the sense that our theory provides both for a programming framework and a programming logic for such agents. An example program is proven correct by using this programming logic.

Research in the UCL Bioinformatics Unit, Prof David Jones, University College London

In this talk some of the research being carried out in the UCL Bioinformatics Unit will be discussed, with a focus on our work on protein structure prediction. The so-called "protein folding problem" is perhaps the biggest of the remaining challenges in molecular biology.The puzzle as to how the complex 3-D structure of a protein is encoded purely by its amino acid sequence is being tackled by both experimentalists and theoreticians alike. Although a full understanding of how proteins self-assemble into their native structures is a long way off, a more tractable sub-problem is the challenge of programming a computer to predict the structure of a given protein from its sequence. In computational terms, the protein folding problem can be described as a "black box" into which a string of 20 different letters is fed and a set of X,Y,Z coordinates for each atom in the protein produced as the output. In this talk, I will review some of the work from my own group in developing different protein folding "black boxes", and how these can help in the annotation of genome sequences.

The Current Opinion of Robots for Landmine Detection, Dr Chandrasekhar Kambhampathi, University of Hull

Anti-Personal landmines are a significant barrier to economic and social development in a number of countries. Several sensors have been developed but each one will probably have to find, if it exists, a specific area of applicability, determined by technological as well as economical or even social factors, and possibly other sensors to work with using some form of sensor fusion. A significant issue concerns the safety of the deminer. For every 5000 mines removed, one deminer is killed. The design of an accurate sensor may reduce the amount of time needed to determine whether a landmine exists, but does not increase the safety of the deminer. Since the safety issues during the eradication process are of great concern, the use and integration of cheap and simple robots and sensors in humanitarian demining has been raised. This removes the deminer from close contact with mines. This seminar is concerned mainly with the work that has been done in the area of robotics and landmine detection, the problems involved, the issues that have been overlooked and the future of robots in the field.

Recent Advances in Dialogue Research, Mr Nick Webb, Natural Language Processing Group

Research into dialogue systems has been ongoing for several decades, often funded and championed by large scale national programs, such as DARPA Communicator in the US. Despite this, advances in dialogue research have been small, both in terms of the usability and performance of such systems.

The community now has access to large scale corpora of dialogues, annotated both for dialogue act structure and in some cases semantic content. The use of such corpora allows us to acquire large scale models of dialogue, and to determine empirically mappings between incoming user utterances and their structure and content. This in turn allows dialogue systems to be broader, more robust, and increasingly conversationally plausible.

This talk will focus on this research at the University of Sheffield in the context of a number of large scale European funded grants.

Incomplete Dynamic Backtracking, Dr Steve Prestwich, University College Cork

What is the essential difference between stochastic local search (LS) and systematic backtracking (BT) that often gives LS superior scalability? Answering this question might lead to new search algorithms combining advantages of both. I argue that the key is LS's lack of commitment to any given variable assignment, and I show that a non-committing (and incomplete) form of backtracking scales exactly like LS. In fact I claim that this is simply LS applied to a different space, offering a clean way of combining LS with techniques such as constraint propagation and relaxation.

Spoken dialogue for controlling home devices, Dr David Milward, Linguamatics Ltd

Spoken dialogue has the potential to improve the usability of individual home devices, to allow convenient control of multiple networked devices, and to enable programming of interactions between devices. However, current dialogue systems tend to be based on form filling or making choices from a fixed set of alternatives. For command and control applications, this kind of dialogue is particularly unnatural. This talk describes some of the challenges imposed by the home domain, and some solutions. In particular we show how the use of a domain model allows for a more easily reconfigurable dialogue system, which allows flexible interaction with a user.The talk will conclude with a demonstration of spoken dialogue control of a simulated home.

Swarms in a Dynamic Environment, Dr Tim Blackwell, University College London

The search and tracking of optima in dynamic environments is a difficult problem for evolutionary algorithms due to diversity loss. However, another population based search technique, particle swarm optimisation (PSO), is well suited to the dynamic search problem. In particular, if some or all of the particles are “charged”, population diversity can be maintained, and good tracking achieved with a simple algorithm. Charged particle swarms are based on an electrostatic analogy – inter-particle repulsions enable charged particles to swarm around a nucleus of neutral particles. This work extends the difficulty of the search tasks by considering a bi-modal parabolic environment of high spatial and temporal severity. Two types of charged swarms and an adapted neutral swarm are compared for a number of different dynamic environments which include extreme ‘needle-in-the-haystack’ cases. The results suggest that charged swarms perform best in the extreme cases, but neutral swarms are better optimisers in milder environments.

Macromolecular Database project, Mr Kim Henrick, European Bioinformatics Institution

The E-MSD, EBI Macromolecular Structure Database for the Protein Data Bank (PDB),, was set up in 1996 to give Europe an autonomous and trusted facility to collect, organise and make available data about biological macromolecular structures and to integrate structure with the other biological databases at the EBI. Since then, the E-MSD group has been working in the following areas:

- Accepting and processing depositions to the (PDB)
- Transforming the PDB flat file archive to a relational database system
- Developing services to search the PDB
- Developing standards for 3D-database information design and models that describe the conceptual framework, in which all data are collected, stored and disseminated.
- In partnership with projects to addresses an urgent need to develop an end user environment to take advantage of the rapid development of high throughput facilities associated with protein crystallography.

The presentation will give an overview of the A-Z approach adopted by the EBI
for the application of data handling techniques from experimentalist through to a final web search system for macromolecular structures via the MSD database components, the relational database and the data warehouse. Both use Oracle as the database engine and are designed and managed using the Oracle case tool Designer.

Statistical Properties of Local Search Landscapes, Prof Colin Reeves, Coventry University

This talk concerns the application of some statistical estimation tools in trying to understand the nature of the combinatorial landscapes induced by local search methods. One interesting property of a landscape is the number of optima that are present. I shall show that it is possible to compute a confidence interval on the number of independent local searches needed to find all optima. By extension, this also expresses the confidence that the global optimum has been found.
In many cases this confidence may be too low to be acceptable, but it is also possible to estimate the number of optima that exist. Theoretical analysis and empirical studies are discussed, which show that it may be possible to obtain a fairly accurate picture of this property of a combinatorial landscape. The approach will be illustrated by analysis of an instance of the flowshop scheduling problem.

Sparse Kernel Methods, Dr Steve Gunn, University of Southampton

A widely acknowledged drawback of many statistical modelling techniques, commonly used in machine learning, is that the resulting model is extremely difficult to interpret. A number of new concepts and algorithms have been introduced by researchers to address this problem. They focus primarily on determining which inputs are relevant in predicting the output. This work describes a transparent, non-linear modelling approach that enables the constructed predictive models to be visualised, allowing model validation and assisting in interpretation. The technique combines the representational advantage of a sparse ANOVA decomposition, with the good generalisation ability of a kernel machine.

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Summer 2002

Summarising scientific articles: The use of robust document analysis, Dr Simone Teufel, University of Cambridge

Most current summarisers still use sentence extraction as their main paradigm. But this assumes that each sentence carries its meaning in isolation -- an assumption that is over simplistic and brings with it many discourse syntactic and semantic problems. My approach to the summarisation of scientific articles (which are thoroughly different from newspaper articles, the text type which is mostly used in the summarisation community) uses machine learning to improve on sentence extraction by adding rhetorical context to each extracted sentence. Current work concentrates on the task of merging sentences of the same rhetorical status, e.g. sentences describing the research goal, into one new, coherent sentence.

Numerical methods for quadratic programming, Professor Nicholas Gould, Rutherford Appleton Laboratory

In this talk, we will describe two new methods for large-scale nonconvex quadratic programming. The first is an interior-point trust-region method, which has proved capable of solving problems involving up to half a million unknowns and constraints. The second, an active-set method, has just been completed, and is intended primarily for the case where a good estimate of the optimal active set can be predicted. Key linear algebraic aspects of both methods will be described, as will the results of tests on a large set of convex and nonconvex quadratic programming examples. Both codes are available as part of the GALAHAD optimisation software library.

Generation eXtravert: language production and personality type, Dr Jon Oberlander, University of Edinburgh

Work within Reeves and Nass's `Computers Are Social Actors' (CASA)
paradigm suggests (a) that computer users reliably attribute personality to computer interfaces, even in text-only environments; and (b) that users apparently prefer interfaces which use language associated with personality traits matching their own. We have been studying in greater detail how dimensions like Extraversion or Introversion influence people's language production. Applying bigram-based techniques from statistical NLP to a corpus of email has replicated earlier findings, and uncovered novel patterns of linguistic behaviour. We discuss implications of these findings for computational models of language production.

An Introduction to Multi-objective Evolutionary Optimisation, Professor Kalyanmoy Deb, University of Birmingham

Many real-world search and optimisation problems are naturally posed as non-linear programming problems having multiple objectives. Due to lack of suitable solution techniques, such problems are artificially converted into a single-objective problem and solved. The difficulty arises because such problems give rise to a set of Pareto-optimal solutions, instead of a single optimum solution. It then becomes important to find not just one Pareto-optimal solution but as many of them as possible. 

Classical methods are not efficient because they require repetitive applications to find multiple Pareto-optimal solutions and in some occasions repetitive applications do not guarantee finding distinct Pareto-optimal solutions. The population approach of genetic algorithms (GAs) allows an efficient way to find multiple Pareto-optimal solutions simultaneously in a single run. In this talk, one implementation of a GA, which uses non-domination concept, will be discussed. Simulation results on a number of test problems and on a couple of engineering design problems will show the efficacy of the method. Clearly, GAs (or other evolutionary search methods) have a niche in solving multi-objective optimisation problems compared to classical methods. This is why multi-objective optimisation using GAs is receiving a growing attention in the recent past. Since this is a comparatively new field of research, in this talk, a number of future challenges in the research of multi-objective optimisation will also be discussed. 

Towards Robots That Develop Concepts, Christopher G. Prince,University of Minnesota Duluth

In humans, concepts provide part of the cognitive apparatus enabling generalization and behavioural flexibility. For example, when we as adults identify a new game, joke, chair, cat, car, or vehicle, we are in part using our conceptual skills. Conceptual skills, in humans, are developed from an early age. In this talk I consider the possibility of robots that developmentally acquire skills with concepts. If we can create robots that develop their own concepts, this may provide better generalization and behavioural flexibility in these systems. Developmental (or epigenetic) robotics is a relatively new area with central tenets of programming robots with only a small set of “innate” skills (e.g., contingency detection), and using task non-specific developmental algorithms (Weng et al., 2001). I review some relevant literature and our initial work in this area.

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Spring 2002

A Bayesian Approach to Realising Sparse Machine Learning Models, Dr Mike Tipping, Microsoft Research

There has been much recent interest (stimulated principally by the properties of the 'support vector machine') in 'sparse' machine learning models. Such models, applicable to both regression and classification, may be specified with a potentially large initial set of basis functions, but as a result of some 'training' procedure, only a fraction of the basis set is ultimately used for prediction purposes. This talk will outline a practical and highly effective such learning procedure derived from a Bayesian inference perspective. I'll review the established 'relevance vector machine' (a Bayesian counterpart to the support vector machine) as well as presenting more recent related research, and attempt to convey the core ideas behind sparse Bayesian learning in general.

Projecting Types over SemiStructured Data, Professor Richard Connor, University of Strathclyde

Semistructured data, and in particular XML which can model it, are fast
becoming perceived as the emerging de facto data format. Query paradigms for such data is a topic in its infancy, however.
Various research projects (e.g. Lorel, Ozone) and commercial standards (e.g. DOM, SAX, XSLT) give mechanisms for extracting information from such data resources. However all of these require interpretation over the semantics of the values modelled, which are presented to the programmer as a tree or graph in all cases. This leads to very important classes of errors, normally trapped by type system mechanisms, to cause failure at run-time or, even worse, to remain undetected and give incorrect answers.

Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun Spoken Dialogue System, Professor Diane Litman, University of Pittsburgh

Designers of dialogue systems face a number of nontrivial choices in dialogue strategy, including choices in initiative and confirmation strategy. We apply Markov decision processes (MDPs) and reinforcement learning to the problem of automated dialogue strategy synthesis. An MDP is built from training data gathered from an "exploratory" system. This provides a model of user reactions to system actions, which is used to choose the optimal among many dialogue strategies. We have applied this methodology in the NJFun spoken dialogue system, yielding experimental results of statistically significant improvements in system performance.

Instructing Robots using Natural Language, Dr Guido Bugmann, University of Plymouth

Future domestic robots will need to adapt to the special needs of their users and to their environment. Programming by natural language will be a key method enabling computer language-naive users to instruct their robots. This talk describes the design of a practical system that uses unconstrained speech to teach a vision-based robot how to navigate in a miniature town. The robot knows a set of primitive navigation procedures that the user can refer to when giving route instructions. A particularity of this project is that the primitive procedures are determined by analysing a corpus of route instructions. These can be very complex procedures for the robot. Recognition errors may occur because the lexicon and the set of primitive procedures is likely not to be closed. These issues and explored solutions are discussed.

Evolutionary and reinforcement learning of fuzzy models in mobile robotics applications, Professor Andrea Bonarini, Politecnico di Milano

Fuzzy models are receiving growing interest to implement control and interpretation systems for autonomous mobile robots. Learning these models improves the design process, and adaptation makes it possible to install mobile robots in new and changing environments. Evolutionary and reinforcement learning are paradigms that fit well the needs of this type of applications, but their use is often left to the crafting ability of the designer, and a methodological support is still lacking. We will present some applications developed at the Politecnico di Milano Artificial Intelligence and Robotics Project, where we are working on the definition of a set of methodological tools to engineer the learning and adaptation processes. Applications include both simulated and real robots, operating in tasks such as prey-predator co-evolution, multi-agent coordination, adaptation to unknown, dynamical and hostile environments (Robocup).

Pigeon navigation: from release experiments to a simulation model, Roswitha Wiltschko, J.W. Goethe University, Frankfurt

This talk will present an overview and first results on a collaborative project between J.W. Goethe University of Frankfurt and the University of Essex on computer simulation of pigeon navigation.

Release experiments with carrier pigeons suggest that pigeons use environmental gradients as navigational factors to determine the correct compass course leading to their home loft, and that they follow that direction using one of their compass mechanisms.

The values of most of the parameters that govern this process - such as required accuracy of the compass sense, required accuracy of detecting gradients, "recomputation" rate, etc - are unknown, and very difficult to assess through experiments with pigeons.

We have therefore built a computer model of pigeon navigation that allows the principled investigation of the influence of individual parameters upon navigation efficiency and reliability.

The talk will present the biological basis of the model, and discuss the accuracy simulation predictions with biological findings.

Background information to this project can be found at

Is That Your Final Answer? : The Role of Confidence in Question Answering Systems, Dr Rob Gaizauskas, University of Sheffield

Question answering (QA) has suddenly become a hot topic in applied natural language research. Driven by the appearance of Web search engines such as "Ask Jeeves", apparently offering capabilities beyond those of conventional search engines, and by the appearance of a QA track in recent Text REtrieval Conference (TREC) evaluations, QA has moved to centre stage in a remarkably short time. Once again, QA raises issues of the relative value of shallower versus deeper models of language when addressing applied language processing tasks.

In this talk I will discuss the methodology and results of the TREC QA track evaluations, describe the approaches we have used in our TREC-8 and TREC-9 QA entries, and end by considering the heretofore neglected issue of confidence: why it is important for QA systems to supply confidence measures for the answers they return and how we go about computing such measures.

TESSA: A Constrained Speech to Sign Language Translation System, Dr Stephen Cox, University of East Anglia

TESSA is a system that aims to aid transactions between a deaf person and a clerk in a Post Office. The system uses a speech recogniser to recognise speech from the Post Office clerk, and then synthesises the recognised phrase in British Sign Language (BSL) using a specially developed avatar. The BSL phrases are pre-stored as parameter files, and can be concatenated to sign variable quantities such as amounts of money, days of the week etc. This approach places restrictions on the translating power of the system, but it is still effective because of the highly formulaic nature of the transactions in a Post Office. The design, development and evaluation of TESSA will be described, and recent developments that allow unconstrained speech input by the clerk will be explained.

Bayesian harmonic models for polyphonic computer music transcription, Simon Godswill, University of Cambridge

There is an increasing requirement for automatic recognition, classification and information retrieval from musical audio in web-based multimedia databases. In this work we study the problem of multiple pitch estimation for polyphonic music. We develop novel Bayesian hierarchical models for musical audio signals comprising several fundamental pitches and their harmonics and incorporate reasonable Bayesian prior information about musical audio. The methods are found to be effective for a number of examples, provided the number of simultaneous sources is not too large, allowing automatic estimation of pitches, number of notes and number of harmonics for each note. We predict that the models developed will find application as the low-level signal model in more complex Bayesian models which incorporate the long-term time-domain behaviour of musical signals. We are currently investigating applications in instrument recognition, transcription and source separation.

Descriptive Modelling: A Rough-Fuzzy Approach, Dr Qiang Shen, University of Edinburgh

The building of intelligent systems for many real-world problems requires modelling and inference methods that entail not only computational efficiency and accuracy, but also user understandability and interpretability. Existing approximate model-based systems typically overlook the significance of such requirements. In this talk, I shall introduce some of the recent work on flexible descriptive modelling and reasoning, based on an integrated use of rough and fuzzy set theories, that attempts to address these requirements. To demonstrate the utility of such work I shall present sample application results, in coping with problems such as plant monitoring, e-text categorisation, and population estimation. The talk will end with some thoughts of potential future developments.

Foundations of Evolutionary Computation, Professor Riccardo Poli, University of Essex

Since Holland's seminal work in the mid seventies and his well known schema theorem, schemata have traditionally been used to explain why genetic algorithms (GAs) and, more recently, genetic programming (GP) work. However, in the last 7 or 8 years the usefulness of schemata has been seriously questioned, and the attention of GA theorists has moved away from schemata to land onto Markov chains.

In this talk, after explaining what schemata and schema theorems are, I will show why one should be interested in schema theories. I will do that by presenting some very recent results including an exact schema theory for GAs and my own schema theory for GP. I will show how these results can be used in important ways in theoretical investigations, which are also very relevant for practitioners. 

I will also illustrate how the exact schema theory for genetic programming supersedes almost all previous work on schemata for GAs, including Holland's schema theorem, thus offering itself as an important step towards the unification for the theoretical foundations of evolutionary computation.

Earth Observation Satellite Mission Management: Experiments with Various Modelling Frameworks and Solving Methods, Mr Gerard Verfaillie, ONERA-CERT, France

The aim of this talk is to discuss the results of the experiments that have been done by using various representation frameworks and various algorithmic methods for modelling and solving large combinatorial constrained optimization problems, that appear when managing the mission of Earth observation satellites. 

The problem of the management of the mission of Earth observation satellites will be presented, with all its physical and operational constraints, especially in the context of the new generation of agile satellites. 

Its nature and its complexity will be then presented, as well as the various modelling frameworks that can be used to represent it: Integer Linear Programming, Constraint Programming, Valued Constraint Satisfaction Problems, Graph Theory.

The various solving methods that have been used to attempt to solve it will be presented too: Greedy Search, Local Search, Tree Search, Dynamic Programming.

Finally, we will try to explain the results obtained with these various modelling frameworks and solving methods and to discuss some promising ways of research in combinatorial constrained optimization.

Finite model theory, complexity theory and program schemes and The MathFIT initiative, Professor Iain Stewart, University of Leicester

Finite model theory is all about what one can say about classes of finite structures (such as graphs, strings and so on) using logic; and computational complexity is all about what one can compute on finite inputs within given resources. There is a very strong link between finite model theory and computational complexity theory (exemplified by Fagin's Theorem that a problem is in NP if and only if it can be defined in existential second-order logic). Often, this link is strongest when the finite structures are (essentially) strings: on arbitrary finite structures, the link between resource-bounded computation and logical definability is nowhere near as clear-cut. In this introductory talk, I will introduce this subject, known as descriptive complexity, and I will also introduce models of computation, program schemes, for computing on arbitrary finite structures, and show how a consideration of these models can lead to new results in finite model theory and descriptive complexity. The talk will be introductory in nature and suitable for a general audience.

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Autumn 2002

Perception and Spatial Reasoning in a Humanoid Robot, Dr Murray Shanahan, Imperial College, London

This talk will describe our progress to date in investigating on a topic fundamental to cognitive robotics, namely the interplay between high-level reasoning and low-level perception. In particular, I'll present ongoing work on spatial reasoning and visual perception for a humanoid robot that has been built at Imperial College. Issues that will be discussed include active perception, top-down versus bottom-up processing of sensor data, and symbol anchoring.

Thorny dialogues: Challenges in interfacing with the nervous system, Dr Francisco Sepulveda, University of Essex

“Easier said than done”. Few areas of endeavour would be more prone to comments of this nature than neural interfaces, though it has not always been this way. The last century saw a tremendous increase in our knowledge of how the nervous system works. Our understanding of neural function went from the merely systemic down to the molecular level, at times giving us the impression that the precious gray matter could be mastered after all. But, alas, our enhanced understanding of the nervous system has yet to have a big impact on our ability to communicate with it. While monitoring and stimulating nerve cells in vitro is very straight forward and has been done for more than fifty years, interfacing with the human nervous system in vivo has proven to be a major challenge. Observed signal to noise ratios are often below unity, signal variability is high and fast, functional maps drift, and interface selectivity is still very poor.
In this seminar I will present the state of the art in neural interfaces. Surface and implanted devices will be addressed with reference to two-way communication with the peripheral and central nervous systems. Special emphasis will be given to brain-computer interfaces.

Formal Method in Software Design, Dr Amnon Eden, University of Essex

The construction of software is generally divided into three levels of abstraction: Architecture, design, and programming. From the perspective of formal modelling, programming has won most of the attention, involving various notations and formal languages (most notably, lambda calculus and turing machines.) The 1990s gave rise to the recognition of software architecture as a separate discipline, and with it to the formalization of architectural styles. But although techniques and methodologies of software design were in wide use since the 1970s, software design has always been perceived as an art rather than science and little formal work has been done in this domain. 

We focus of formalization of design techniques, most prominently of object-oriented design patterns. We depict a formal ontology for the discussion in object-oriented design and in the specification of patterns and programs at the design level.

Unobtrusive Augmentation of Everyday Environments with Computing, Professor Hans Gellersen, Lancaster University

Ubiquitous computing is a new paradigm for interactive systems that moves computers into the background while using them to support people's activities and interactions. The ubiquitous computing paradigm is underpinned by the development of components small enough to be embedded in practically anything, of networks that provide dense inter-connection of large numbers of objects, and of sensing technologies that enable systems to become contextually aware. A fascinating research challenge is to leverage these trends to build systems and technologies that transform everyday environments unobtrusively into smart spaces.

This talk will discuss innovative technology concepts and prototypes that we have developed to explore how we may facilitate everyday environments with ubiquitous computing technology. The examples we investigate are augmentation of ordinary walls as network for the things attached; embedding and application of load sensing in common artefacts such as tables; and rapid prototyping of networked smart objects with the Smart-Its device platform.

Colossus and the breaking of the German Lorenz cipher, Mr Tony Sale/Anthony E Sale, Hon. FBCS ex Museums Director, Bletchley Park

Tony will describe the German Lorenz cipher machine and how a dreadful mistake by the German operators, in 1941, led to its secrets being discovered by Bill Tutte in Bletchley Park, without ever having seen the machine. This led to Colossus being designed by Tommy Flowers at Dollis Hill, the Post Office Research laboratories. 10 Colossi were eventually built and Tony will describe their effect on the Allied war effort. He will finish by describing his team's efforts in rebuilding Colossus and show a short video of it working. Further information can be found on his web site,

Smelling mobile robots, Professor GurvinderVirk, University of Leeds

The seminar will consider biologically inspired design of mobile robots. The focus will be on navigational aspects and how animals use a variety of techniques to search for food and for mates. These methods have been shown to be remarkably robust and able to work in realistic and practical situations. In contrast, man-made navigational methods, based on a number of AI methods, have failed miserably to work other than in perfect laboratory conditions. In view of this it seems useful to turn to nature and see if these biological methods can be understood and then mapped onto robotic machines. Much of the work in this area is biased towards vision systems, which is very compute intensive. Prof Virk has been investigating an alternative strategy for the navigation of mobile robots based on smelling.

Work in this area will be described where an innovative smell based navigational strategy has been developed. The work described will include both fundamental theoretical ideas of how the unstable diffusion fields can be tolerated to produce reliable strategies. The method uses a biased random walking strategy. Theoretical simulation and the practical robotic results will be described.

Research at the Bioinformatics Research Centre, University of Glasgow, Professor David Gilbert, University of Glasgow

In this talk I will describe some current research in the newly established Bioinformatics Research Centre. Attached to the Department of Computing Science, this interdisciplinary Centre will be physically located in the Institute of Biomedical and Life Sciences from spring 2003, and hosts researchers from both Computing Science and Life Sciences.

Specifically, I will describe some ongoing funded research projects, highlighting the computational issues which are characteristic of data-driven bioinformatics research:

- a graph-based approach to modelling and analysis of protein structures, involving machine learning over graph data structures
- the use of concurrency theory to simulate and analyse biochemical networks
- the use of functional genomics for the analysis of hypertension

I will also present some views on the ways in which the GRID may play a part in enabling an eScience approach to research in Bioinformatics.

Algorithms and applications of worst-case design, Professor Berc Rustem, Imperial College, London

The talk will describe an algorithm for the discrete minimax problem and an algorithm for the continuous minimax problem. Both are quasi-Newton algorithms but are radically different in the construction of the search directions. The convergence properties will be outlined. Time permitting, the application of these algorithms to worst-case design problems in macroeconomics, finance and process systems engineering will also be discussed.

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Spring 2001

The Evolution of Telecommunications Networks, Professor M O'Mahony, Department of Electronic Systems Engineering, University of Essex

The past three years has seen an exponential growth in the use of Internet and mobile telephones. This has led to an enormous demand for capacity within telecommunications networks. The only way this can be met economically is through the use of advanced technologies for transmission and switching based on optical techniques. Telecommunications was transformed in the 1970s when the inventions of the semiconductor laser and the optical fibre enabled the realisation of low cost high capacity transmission. In 2001, it is likely that optical switching will cause a similar leap in capabilities. In the longer term optical packet switching will merge with electronic packet switching to provide a high capacity global network controlled by the user.

Computational Finance at University of Essex, Professor E Tsang, Department of Computer Science

This talk gives an overview of the multi-disciplinary research in computational finance at the University of Essex.  I shall explain the EDDIE architecture and its applications.  I shall also explain our new research direction in adaptive behavioural economics (the so called neo-classical economics by Holland's followers)

Applying Constraints Technology at British Airways, Dr C Doel, Parc Technologies Limited

British Airways has now installed several applications, which depend upon constraint technology. Two successful applications are "Aircraft Swapper" and "Operations Retimer". Prototype software running these applications was developed at IC-Parc some years ago. Since then, an enormous amount of ground has been covered in order to achieve delivery and acceptance of these two applications. As an employee of British Airways, I was closely involved in the delivery phase of these applications. I shall chart the progress of delivery phase of the Retimer projects, give some limited background to the mathematical basis of the constraint and search mechanisms involved and demo the product.

Automatically Deriving and Modelling Invariant Information from Dynamic Planning Domain Models, Professor G Kelleher, Research and Graduate School Information

Domain constraints are central to many AI based planning systems, and of relevance to a number of other areas. The majority of current systems assume that these constraints will be provided by the domain author. Experience suggests that this can be a difficult and error prone task as domain constraints typically encode implicit and obvious information, which is rarely articulated. Recent work has begun to address the automatic synthesis of domain constraints from STRIPS-like operators and to extend these techniques to more expressive operator languages, such as ADL. The talk reviews this work and discusses potential applications of the ideas both within and outside planning systems.

The Path to Personalisation (or Why Intelligent Information Agents Must Learn), Dr P Edwards, Departments of Computer Science, Aberdeen University

Interest in personalisation technologies has grown rapidly in recent years, inspired by the development of the Web, E-Business and mobile communications. Profiling a user (or group of users) is an essential pre-requisite of the personalisation process. The goal-of user profiling can be simply stated as follows: to acquire a model of a user encoded in some representation which can be utilised to deliver a targeted (or personalised) service, product or outcome. Profiling techniques in use at present fall into two broad categories: content-based and collaborative. Content-based methods extract information (such as keywords) from items manipulated by a user and create a profile which links this information with the user's preferences concerning those items. Collaborative methods attempt to characterise the preferences of individual users within a community, and then identify commonalities between users. 

Recently, a number of researchers have combined content-based and collaborative profiling to create hybrid profiling methods. In this presentation, I shall argue that software agents which aim to offer personalised services should do so through the application of machine learning technologies. I will present recent work on systems such as MAGI, LAW and COBRA all of which employ learning algorithms to acquire and adapt user-profiles.

Bayesian Learning of Model Structure, Dr Z Ghahramani, Gatsby Computational Neuoscience Unit, University College London

One way to do unsupervised machine learning is to build a probabilistic generative model of the sensory data. I will describe a Bayesian approach to learning the structure of such models. This approach resolves the tension between fitting the data well and keeping the models simple by averaging overall possible settings of the model parameters. Unfortunately, these adverse are usually intractable, resulting in the need for approximations. I will present variational approximations, which are deterministic, generally fast, and have an objective function, which is guaranteed to increase monotonically. Some theoretical results show how variational optimisation generalises both the EM algorithm and belief propagation algorithms. This approach can be used (1) to automatically infer the most probable number of clusters and the intrinsic dimensionality of each cluster, (2) to find the number of sources in a blind source separation (ICA) problem, (3) to infer the state dimensionality of a linear dynamical system, and (4) to optimise over the structure of a mixture of experts network.

Bionic Sonar for Feature and Texture Recognition for Robot Navigation, Dr P Probert, Department of Engineering Science, University of Oxford

This talk will describe how advances in sonar sensors based on the observation of biological systems enables sonar to provide reliable, efficient sensing for navigation. It will show how a physical understanding of the sensor drives the development of a classifier to recognise different features and textures in the environment. Examples will be given on the classification of robot pathways and on typical indoor scenes.

The Active Information Repository, Dr P Watson, Department of Computer Science, University of Newcastle upon Tyne

In this talk I will describe the design of a scalable Grid node to support computation on information stored in databases. It is envisaged that huge amounts of information, both structured and semi- structured, will be made available on The Grid. Simple client-server style access to the information server will be inadequate, as the network connecting the client to the server will not have the required bandwidth to support computations that may access Petabytes of data. A solution that allows clients to submit computations to be run locally to the data is therefore required. The Active Information Repository is designed for this. Data is stored in a scalable object database server and computations, in the form of agents, can be sent by clients for execution on a scalable agent execution server that is tightly coupled, through a high bandwidth network, to the database server. The talk will describe the rationale for this approach, and the design of the major components: the parallel database server, and the parallel agent execution server.

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Autumn 2000

Ten Steps to Human-Level Artificial Intelligence, Dr Murry Shanahan, Department of Electrical and Electronic Engineering, Imperial College

The focus of this seminar will be a speculative list of ten crucial advances I think we need to make in order to achieve human-level artificial intelligence, starting with the construction of simple robots that can reason with their actions, and ending with metaphor, creativity and original thought. On a more concrete level, I will present Imperial College's ongoing work on cognitive robotics, which is a humble beginning to this research programme.

Local Search using Neighbourhoods of Exponential Size, Dr Chris Potts, Faculty of Mathematical Studies

A new neighbourhood search technique is introduced that uses dynamic programming to search an exponential size neighbourhood in polynomial time. This technique is called dynasearch. We apply dynasearch to three types of sequencing problems; the total weighted tardiness scheduling problem, the linear ordering problem and the travelling salesman problem. Computational results show that, for restarts close to previous local minima, dynasearch is significantly more effective than classical first improve or best improve descent. As a consequence, our proposed algorithms use dynsearch with an iterated local search framework, where each descent is started a few random moves away from previous local minima. Computational results show that the proposed dynasearch algorithms are more effective than other known local search heuristics for the total weighted tardiness problem and for the linear ordering problem, and are competitive with other methods for the travelling salesman problem. 

Global Optimisation and the Aircraft Routing Problem, Dr Mike Bartholomew-Biggs, Mathematics Department, University of Hertfordshire

A simple model of the routing problem involves the determination of "waypoints" between straight line stages of a route between prescribed start and finish points. An optimum route is one which has minimum length consistent with the avoidance of no-fly zones and subject to flyability constraints such as limitations on turn angle. This problem can be formulated as an optimization calculation which turns out to have multiple solutions. The objective function, in practice, is not easily differentiable because it will involve interpolation in tabulated geographical data. Therefore we need to consider direct-search methods for global optimization. An algorithm called DIRECT (Jones et al) seems a promising contender for this task, and the talk will describe the application of this, and other, techniques to some sample problems. 

Computational Models of the Emergence of Linguistic Diversity Problems using Artificial Life to Answer Real Problems, Dr Daniel Livingstone, Department of Computing and Information Systems, University of Paisley

Over the past decade there has been a dramatic rise in interest in Artificial Life and a co- incident resurgence of interest in the evolution of language and languages. A significant, and growing, number of researchers are using the former to try to answer a variety of questions relating to the latter. My own work has focussed on the emergence and evolution of linguistic diversity, where debate continues amongst linguists on the causes of and reasons for language diversity. The models I have developed show that social function and adaptive benefits are not necessary forces in linguistic evolution, and these are reviewed. However, other computational models have results that lead to quite different conclusions. A real problem for this work is how to resolve the dispute between such contradictory findings. I conclude by looking at how I can strengthen my own findings, while reviewing some weakness of the various models mentioned (my own included) and of the Artificial Life method. 

Evolutionary Algorithms for Bus and Train Driver Scheduling, Dr Raymond Kwan, School of Computer Studies, University of Leeds

At present the most successful systems for bus and train driver scheduling are based on the set covering model solved by integer linear programming (ILP) techniques. However, these systems still rely on heuristics to cut down the solution space in finding all-integer solutions. Large problems may have to be subdivided, thereby losing optimality. The search for an all-integer solution might be terminated prematurely because of practical computational limits. Meta-heuristic approaches have the potential of yielding a quicker and more robust method. In particular, Genetic Algorithm and its derivatives have received much research attention in recent years. Early research has concentrated on designing a suitable genetic representation of the problem and the algorithms derived were relatively simple and straightforward. While the computational speed was very good and all-integer solutions were always obtainable, the quality of solutions could not match up with that obtained by ILP. Recent research has focussed on hybrid algorithms that incorporate two main strategies. The first strategy is to exploit strong domain knowledge, e.g. key patterns of work piece combinations, so that important combinatorial characteristics in the population members can be identified and utilised in the evolutionary process. The second strategy is to fuzzify the criteria in assessing the fitness of the population members, so that the potential of intermediate solutions as stepping stones could be better evaluated. Experiments and results based on real lift problem data will be presented. 

Proof Planning - Case Studies and Future Directions, Dr M Kerber, School of Computer Science, University of Birmingham

One can distinguish two major approaches to automated theorem proving, machine-orientated methods like the resolution method and human-orientated methods. Proof planning as first introduced by Alan Bundy belongs to the second category. It tries to model the cognitive ability of humans to perform plausible reasoning, that is, to master huge search spaces by making good guesses. The process of plausible reasoning is viewed as a planning process; its strength heavily relies on the availability of domain specific problem solving knowledge.
In the talk, I want to address mainly the following questions: what is proof planning, what are successful applications of it, what are major open problems, and how can they be addressed.

Solving Non-Boolean Satisfiability Problems, Dr A Frisch, University of York

State-of-the-art procedures for determining the satisfiability of formulas of Boolean logic have developed to the point where they can solve problem instances that are large enough to be of practical importance. Many of the problems on which satisfiability procedures have been effective are non-Boolean in that they are most naturally formulated in terms of variables with domain sizes greater than two. Boolean satisfiability procedures have been applied to these non-Boolean problems by reformulating the problem with Boolean variables. An alternative, previously unexplored approach, is to generalise satisfiability procedures to handle non-Boolean formulas directly. This talk compares these two approaches. We begin by presenting two procedures that we have developed for solving non- Boolean formulas directly. We also present three methods for systematically transforming non-Boolean formulas to Boolean formulas. Finally, experimental results are reported on using all these methods to solve random formulas, graph colouring problems and planning problems. 

A Proof Planner based on Methodicals and Methodical Continuations, Dr Julian Richardson, Computing and Electrical Engineering, Heriot Watt University

LambdaClam is a higher-order proof planner developed at the University of Edinburgh. Planning strategies are hierarchical, defined using methodicals, which are are analogues of tacticals in tactic-based theorem proving. We can define a semantics for methodical expressions which is naturally interpreted as a depth-first planning algorithm. This is efficient, but there is no notion of proof state - when planning the subgoals of a goal, each child has no access to information held in the other children. There are several reasons, notably for providing a user interface, and for implementing proof plan critics, why we need to able to access the whole proof state during planning. I define transformation rules, which allow methodical expressions to be converted to an equivalent form consisting of an atomic method to apply, and a continuation to apply afterwards. I will describe how the continuation mechanism provided by these transformation rules has been used to define a partial proof plan data structure and a single-stepping planner. This is done in a declarative way, which allows us to retain the advantages of hierarchical proof planning, and of LambdaProlog's bat-in mechanisms for handling variable binding and goal implication. I have proved that the new planning algorithm is equivalent to the standard planning algorithm. The partial proof plan data structure and single-stepping planner are both implemented in LambdaClam. They provide the infrastructure for a user interface, and for proof plan critics.

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