A heuristic approach for solving decentralized-POMDP: assessment on the pursuit problem
Proceedings of the 2002 ACM symposium on Applied computing
Sequential Optimality and Coordination in Multiagent Systems
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Learning to Cooperate via Policy Search
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
The Complexity of Decentralized Control of Markov Decision Processes
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
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We investigate the idea of having groups of agents coevolving in order to iteratively refine multi-agent plans. This idea we called coevolution is formalized and analyzed in a general purpose and applied to the stochastic control frameworks that use an explicit model of the world,: coevolution can directly be adapted to the frameworks of Multi-Agent Markov Decision Processes (MMDP) and Multi-Agent Partially Observable MDP (MPOMDP). We also consider the decentralized version of MPOMDP (DEC-POMDP) which is known to be a difficult problem,: we show that the coevolution approach can be applied if we restrict the search to memoryless policies. We evaluate our coevolutive approach experimentally on a typical multi-agent problem.