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When ants play chess (Or can strategies emerge from tactical behaviours?)

  • Alexis Drogoul
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 957)

Abstract

Because we think that plans or strategies are useful for coordinating multiple agents, and because we hypothesise that most of the plans we use are build partly by us and partly by our immediate environment (which includes other agents), this paper is devoted to the conditions in which strategies can be viewed as the result of interactions between simple agents, each of them having only local information about the state of the world. Our approach is based on the study of some examples of reactive agents applications. Their features are briefly described and we underline, in each of them, what we call the emergent strategies obtained from the local interactions between the agents. Three examples are studied this way: the eco-problem-solving implementations of Pengi and the N-Puzzle, and the sociogenesis process occurring in the artificial ant colonies that compose the MANTA project. We then consider a typical strategical game (chess), and see how to decompose it through a distributed reactive approach called MARCH. Some characteristics of the game are analysed and we conclude on the necessity to handle both a global strategy and local tactics in order to obtain a decently strong chess program.

Keywords

Global Strategy Human Player Natural Coloni Simple Agent Emergent Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Bibliography

  1. (Brooks 1987).
    R. Brooks, “Planning is just a way of avoiding figuring out what to do next”, MIT Working Paper 303,1987.Google Scholar
  2. (Delaye & al. 1990)
    C. Delaye, J. Ferber & E. Jacopin “An interactive approach to problem solving” in Proceedings of ORSTOM'90, November 1990.Google Scholar
  3. (Drogoul & al. 1991)
    A. Drogoul, J. Ferber, E. Jacopin “Viewing Cognitive Modeling as Eco-Problem-Solving: the Pengi Experience”, LAFORIA Technical Report, n∘ 2/91Google Scholar
  4. (Drogoul & Ferber 1992).
    A. Drogoul & J. Ferber, “Multi-Agent Simulation as a Tool for Modeling Societies: Application to Social Differentiation in Ant Colonies”, in Proceedings of MAAMAW '92 (forthcoming “Decentralized AI IV”).Google Scholar
  5. (Drogoul & al. 1992b)
    A. Drogoul, J. Ferber, B. Corbara, D. Fresneau, “A Behavioral Simulation Model for the Study of Emergent Social Structures” in Towards a Practive of Autonomous Systems, F.J. Varela & P. Bourgine Eds, pp. 161–170, MIT Press.Google Scholar
  6. (Drogoul & Dubreuil 1992).
    A. Drogoul & C. Dubreuil “Eco-Problem-Solving model: Results of the N-Puzzle”, in (Werner & Demazeau 1992), pp 283–295.Google Scholar
  7. (Drogoul & Dubreuil 1993).
    A. Drogoul & C. Dubreuil “A Distributed Approach to N-Puzzle Solving”, to appear in the proceedings of the 13th DAI Workshop.Google Scholar
  8. (Ferber & Drogoul 1992).
    J. Ferber & A. Drogoul, “Using Reactive Multi-Agent Systems in Simulation and Problem Solving”, in “Distributed Artificial Intelligence: Theory and Praxis”, L. Gasser eds.Google Scholar
  9. (Wavish 1992).
    P. Wavish “Exploiting Emergent Behaviour in Multi-Agent Systems” in (Werner & Demazeau 1992).Google Scholar
  10. (Werner & Demazeau 1992).
    E. Werner & Y.Demazeau, “Decentralized AI 3”, North-Holland, June 1992.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Alexis Drogoul
    • 1
  1. 1.LAFORIA, Boîte 169Université Paris VIParis cedex 05

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