Abstract
For most decision-coordinating problems, it is impossible to find specific knowledge which allows a general system to construct always the best solution. This is why such a system has to search among the good solutions it can generate. As for the construction process, it is then very useful to use specific knowledge for the improvement process. This possibility has been implemented in the MARECHAL system and applied to a game with interesting results.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Barbuceanu, M. & Fox, M.S. Capturing And Modeling Coordination Knowledge For Multi-Agent Systems. International Journal of Cooperative Information Systems, 1996.
Bouzy, B. & Cazenave, T. Computer Go: An AI-Oriented Survey. To appear in Artificial Intelligence 2001/2002.
Glover, F. Tabu search - Part 1. ORSA journal on computing, 1989; (1–3): 190–206.6 For instance, the best computer programs for the game of Go have a beginner level, but this game is more difficult because of the long experience (over 1000 years) of humans in this domain (see [2] for the Go problem in Artificial Intelligence).228
Goldberg, D. Genetic algorithms in search, optimisation and machine learning. Addison Wesley, 1989.
Jennings N.R. Toward a Cooperation Knowledge Level for Collaborative Problem Solving. In Proceedings 10-th European Conference on AI, Vienna, Austria, 1992; 224–228.
Laird, J.E., Newell, A. & Rosenbloom, P.S. SOAR: An architecture for general intelligence. Artificial Intelligence 33, 1987; 1–64.
Maes, P. & Nardi, D. Meta-level Architectures and Reflection. Elsevier Science Publishers B. V., 1988.
Pannürec, T. Gestion implicite de la concurrence dans un système à base de tableau noir. Actes du colloque Intelligence Artificielle de Berder, rapport interne LIP6 n°2000/002, 2000; 42–53.
Pitrat, J. Mütaconnaissance, futur de l’intelligence artificielle. Hermès, 1990.
Pitrat, J. Monitorer la recherche d’une solution. Actes du colloque Intelligence Artificielle de Berder, rapport interne LIP6 n°2000/002, 2000; 3–15.
Pitrat, J. An intelligent system must and can observe his own behavior. Cognitiva 90, Elsevier Science Publishers, 1991; 119–128.
Schiex, T., Fargier, H. & Verfaillie, G. Valued Constraint Satisfaction Problems: Hard and Easy Problems. In Proc. of the 14th International Joint Conference on Artificial Intelligence (IJCAI-95), Montreal, Canada, 1995; 631–637.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag London
About this paper
Cite this paper
Pannérec, T. (2002). Using Meta-level Knowledge to Improve Solutions in Coordination Problems. In: Bramer, M., Coenen, F., Preece, A. (eds) Research and Development in Intelligent Systems XVIII. Springer, London. https://doi.org/10.1007/978-1-4471-0119-2_17
Download citation
DOI: https://doi.org/10.1007/978-1-4471-0119-2_17
Publisher Name: Springer, London
Print ISBN: 978-1-85233-535-9
Online ISBN: 978-1-4471-0119-2
eBook Packages: Springer Book Archive