Subjective approximate solutions for decentralized POMDPs

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Subjective approximate solutions for decentralized POMDPs
A problem of planning for cooperative teams under uncertainty is a crucial one in multiagent systems. Decentralized partially observable Markov decision processes (DECPOMDPs) provide a convenient, but intractable model for specifying planning problems in cooperative teams. Compared to the single-agent case, an additional challenge is posed by the lack of free communication between the teammates. We argue, that acting close to optimally in a team involves a tradeoff between opportunistically taking advantage of agent’s local observations and being predictable for the teammates. We present a more opportunistic version of an existing approximate algorithm for DEC-POMDPs and investigate the tradeoff. Preliminary evaluation shows that in certain settings oportunistic modification provides significantly better performance. Categories and Subject Descriptors I.2.11 [Artificial Intelligence]: Distributed Artificial Intelligence—Coherence and Coordination General Terms Algorithms Keyw...
Anton Chechetka, Katia P. Sycara
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where ATAL
Authors Anton Chechetka, Katia P. Sycara
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