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International Summer School on AI Planning 2002

Planning with Resources

September 16-22, 2002
Halkidiki, Greece




Lecturer:

Philippe Laborie , ILOG , France


Abstract:

A resource can be defined as any substance or (set of) object(s) whose cost or available quantity induce some constraint on the operations that use it. A resource can for instance represent a machine which can only perform one operation at a time, the fuel contained in the tank of a plane or the workforce or the money available to perform a given project. In the context of planning with resources, a solution plan is defined as a plan that achieves the goals while allocating resources to operations in such a way that all resource constraints are satisfied. As in the real life resources often have a limited availability, it's not surprising that resource constraints play a key role in practical planning.

The first part of the course will provide a definition of resources in the context of planning and review the state of the art of planning with resources. The second part will focus on one of the most promising approach for dealing with resources in planning which relies on the application of constraint-based techniques in Partial-Order or Hierarchical Task Network planning.


About the lecturer:

Dr. Philippe Laborie graduated from Ecole Nationale Supérieure des Télécommunications (Paris) in 1992, and received a Ph.D. in Artificial Intelligence from LAAS/CNRS (Toulouse) on the integration of A.I. Planning and Scheduling in 1995. He is one of the developers of the IXTET Planning system. He then worked for two years as post-doctoral fellow at Electricité de France (Paris) and INRIA/IRISA (Rennes) on the Supervision and Diagnosis of complex systems (telecommunication and power distribution networks). His main scientific interests include planning, scheduling, supervision and diagnosis of complex systems and more generally, all decision problems dealing with time. Since 1998 he has been at ILOG S.A. in Gentilly, France, where he currently holds the position of Principal Scientist.

E-mail: plaborie@ilog.fr


Outline:

Here is a preliminary overview of the course. Note that this is subject to minor changes.


Some Planning Systems on the Web:

ASPEN http://www-aig.jpl.nasa.gov/public/planning/aspen/aspen_index.html
BlackBox/SatPlan http://www.cs.washington.edu/homes/kautz/blackbox
CPlan http://ai.uwaterloo.ca/~vanbeek
EXCALIBUR http://www.ai-center.com/projects/excalibur
FF http://www.informatik.uni-freiburg.de/~hoffmann/ff.html
GP-CSP http://rakaposhi.eas.asu.edu/gp-csp.html
Graphplan http://www-2.cs.cmu.edu/~avrim/graphplan.html
GRT http://www.csd.auth.gr/~lpis/GRT/main.html
HSP http://www.ldc.usb.ve/~hector
IPP http://www.informatik.uni-freiburg.de/~koehler/ipp.html
IXTET http://www.laas.fr/RIA/IxTeT/ixtet-planner.html (in French)
LPG http://prometeo.ing.unibs.it/lpg
LPSAT http://www.cs.washington.edu/ai/lpsat.html
Metric-FF http://www.informatik.uni-freiburg.de/~hoffmann/metric-ff.html
O-Plan http://www.aiai.ed.ac.uk/~oplan
parcPLAN http://www-icparc.doc.ic.ac.uk/parcPlan
RAX http://ic.arc.nasa.gov/projects/remote-agent
Sapa http://rakaposhi.eas.asu.edu/sapa.html
SHOP http://www.cs.umd.edu/projects/shop
Sipe http://www.ai.sri.com/~sipe
STAN http://www.dur.ac.uk/computer.science/research/stanstuff/stanpage.html
TP4 http://www.ida.liu.se/~pahas/hsps
UCPOP http://www.cs.washington.edu/ai/ucpop.html
UMCP http://www.cs.umd.edu/projects/plus/umcp


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