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IEEE CIG 2005 Keynote Talk

There are four keynote talks arranged:


 Is Progress Possible?

Professor Jordan B. Pollack
Dynamic & Evolution Machine Org
Computer Science Department
Brandeis University


For the past decade my students and I have worked on coevolutionary learning, both in theory and in applications such as learning game strategies in Tic Tac Toe or Backgammon, solving problems like sorting networks and CA rules, and designing robot bodies and Brains.

Coevolution tries to formalize a computational "arms race" which would lead to the emergence of sophisticated design WITHOUT an intelligent designer, or his fingerprints left in the choice of data representations and fitness function. Coevolution often takes the shape of a game tournament where the players who do well replicate (with mutation) faster than the losers. The fitness function, rather than being absolute, is thus relative to the current population. We have had successes, but we find that often, the competitive dynamics lead to winner-take-all equilibria, boom and bust cycles of memory loss, and mediocre stable states where an oligarchy arises which survives by excluding innovation rather than embracing it. Many researchers have proposed algorithmic methods for overcoming these limitations, involving diversity maintenance, memory for elite players, and so forth, but something is wrong if we have yet to have a convincing mathematical or computational demonstration that competition without central government can lead to sustained innovation.

Is there a missing principle, a different mechanism design in which self-interested players can optimize their own utility, yet together the population keeps improving at the game? If so, and if we discover this in the realm of computational games, would it transfer it to human social organization?


Creating Intelligent Agents through Neuroevolution

Professor Risto Miikkulainen
The University of Texas at Austin


The main difficulty in creating artificial agents is that intelligent behavior is hard to describe. Rules and automata can be used to specify only the most basic behaviors, and feedback for learning is sparse and nonspecific. Intelligent behavior will therefore need to be discovered through interaction with the environment, often through coevolution with other agents. Neuroevolution, i.e. constructing neural network agents through evolutionary methods, has recently shown much promise in such learning tasks. Based on sparse feedback, complex behaviors can be discovered for single agents and for teams of agents, even in real time. In this talk I will review the recent advances in neuroevolution methods and their applications to various game domains such as othello, go, robotic soccer, car racing, and video games.


Challenges in Computer Go

Professor Martin Mueller
Department of Computer Science
University of Alberta

Computer Go has been described as the "final frontier" of research in classical board games.  The game is difficult for computers since no satisfactory evaluation function has been found yet.  Go shares this aspect with many real-life decision making problems, and is therefore an ideal domain to study such difficult domains.  This talk discusses the challenges of Computer Go on three levels: 1. incremental work that can be done to improve current Go programs, 2. strategies for the next decade, and 3. long term perspectives.


Opponent Modelling and Commercial Games

Professor Jaap van den Herik
Department of Computer Science
Universiteit Maastricht

To play a game well a player needs to understand the game.  To defeat an opponent, it may be sufficient to understand the opponent's weak spots and to be able to exploit them. In human practice, both elements (knowing the game and knowing the opponent) play an important role.  This article focuses on opponent modelling independent of any game.  So, the domain of interest is a collection of two-person games, multiperson games, and commercial games.  The emphasis is on types and roles of opponent models, such as speculation, tutoring, training, and mimicking characters.  Various implementations are given.  Suggestions for learning the opponent models are described and their realization is illustrated by opponent models in game-tree search.  We then transfer these techniques to commercial games.  Here it is crucial for a successful opponent model that the changes of the opponent's reactions over time are adequately dealt with.  This is done by dynamic scripting, an improvised online learning technique for games.  Our conclusions are (1) that opponent modelling has a wealth of techniques that are waiting for implementation in actual commercial games, but (2) that the games' publishers are reluctant to incorporate these techniques since they have no definitive opinion on the successes of a program that is outclassing human beings in strength and creativity, and (3) that game AI has an entertainment factor that is too multifaceted to grasp in reasonable time.

<proceedings paper co-authored with H.H.L.M. Donkers, P.H.M. Spronck>