A heuristic approach for solving decentralized-POMDP: assessment on the pursuit problem

  • Authors:

  • Iadine Chades;Bruno Scherrer;François Charpillet

  • Affiliations:

  • MAIA Team, LORIA, B.P. 239 -54506, Vandoeuvre-Les-Nancy, France;MAIA Team, LORIA, B.P. 239 -54506, Vandoeuvre-Les-Nancy, France;MAIA Team, LORIA, B.P. 239 -54506, Vandoeuvre-Les-Nancy, France

  • Venue:
  • Proceedings of the 2002 ACM symposium on Applied computing
  • Year:
  • 2002

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Abstract

Defining the behaviour of a set of situated agents, such that a collaborative problem can be solved is a key issue in multi-agent systems. In this paper, we formulate this problem from the decision theoretic perspective using the framework of Decentralized Partially Observable Markov Decision Processes (DEC-POMDP). Formulating the coordination problem in this way provides a formal foundation for study of cooperation activities. But, as it has been recently shown solving DEC-POMDP is NEXP-complete and thus it is not a realistic approach for the design of agent cooperation policies. However, we demonstrate in this paper that it is not completely desperate. Indeed, we propose an heuristic approach for solving DEC-POMDP when agents are memory-less and when the global reward function can be broken up into a sum of local reward functions. We demonstrate experimentally on an example (the so-called pursuit problem) that this heuristic is efficient within a few iteration steps.