Communication for Improving Policy Computation in Distributed POMDPs

Distributed Partially Observable Markov Decision Problems (POMDPs) are emerging as a popular approach for modeling multiagent teamwork where a group of agents work together to jointly maximize a reward function. Since the problem of finding the optimal joint policy for a distributed POMDP has been s...

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Veröffentlicht in:	Autonomous Agents and Multiagent Systems: Proceedings, 3rd International Joint Conference, New York City, New York, 2004. S. 1098 - 1105
Hauptverfasser:	Nair, Ranjit, Roth, Maayan, Yohoo, Makoto
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	Washington, DC, USA IEEE Computer Society 19.07.2004
Schriftenreihe:	ACM Conferences
Schlagworte:	Computing methodologies > Artificial intelligence > Distributed artificial intelligence > Cooperation and coordination Computing methodologies > Artificial intelligence > Distributed artificial intelligence > Multi-agent systems Mathematics of computing > Probability and statistics > Probabilistic representations > Markov networks Mathematics of computing > Probability and statistics > Stochastic processes > Markov processes Theory of computation > Theory and algorithms for application domains > Machine learning theory > Markov decision processes
ISBN:	9781581138641, 1581138644
Online-Zugang:	Volltext
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