Authors:
Arnaud Canu
and
Abdel-Illah Mouaddib
Affiliation:
Université de Caen Basse-Normandie, UMR 6072 GREYC and F-14032 Caen, France
Keyword(s):
Markovian decision process, Game theory and applications, Multiagent decision making, Co-operation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Computational Intelligence
;
Concurrent Co-Operation
;
Enterprise Information Systems
;
Evolutionary Computing
;
Game Theory Applications
;
Soft Computing
Abstract:
This paper introduces DyLIM, a new model to describe partially observable multiagent decision making problems under uncertainty. DyLIM deals with local interactions amongst the agents, and build the collective behavior from individual ones. Usually, such problems are described using collaborative stochastic games, but this model makes the strong assumption that agents are interacting all the time with all the other agents. With DyLIM, we relax this assumption to be more appropriate to real-life applications, by considering that agents interact sometimes with some agents. We are then able to describe the multiagent problem as a set of individual problems (sometimes interdependent), which allow us to break the combinatorial complexity. We introduce two solving algorithms for this model and we evaluate them on a set of dedicated benchmarks. Then, we show how our approach derive near optimal policies, for problems involving a large number of agents.