A Heuristic Approach for Solving Decentralized-POMDP: Assessment on the Pursuit Problem

Iadine Chadès 1 Bruno Scherrer 1 François Charpillet 1
1 MAIA - Autonomous intelligent machine
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
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 memoryless 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.
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Rapport
[Intern report] A01-R-017 || chades01a, 2001, 6 p
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Soumis le : mardi 26 septembre 2006 - 14:49:29
Dernière modification le : jeudi 11 janvier 2018 - 06:19:50

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  • HAL Id : inria-00100691, version 1

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Iadine Chadès, Bruno Scherrer, François Charpillet. A Heuristic Approach for Solving Decentralized-POMDP: Assessment on the Pursuit Problem. [Intern report] A01-R-017 || chades01a, 2001, 6 p. 〈inria-00100691〉

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