Decentralized Markov Decision Processes with Event-Driven Interactions

Decentralized MDPs provide a powerful formal framework for planning in multi-agent systems, but the complexity of the model limits its usefulness. We study in this paper a class of DEC-MDPs that restricts the interactions between the agents to a structured, event-driven dependency. These dependencie...

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Veröffentlicht in:	Autonomous Agents and Multiagent Systems: Proceedings, 3rd International Joint Conference, New York City, New York, 2004. S. 302 - 309
Hauptverfasser:	Becker, Raphen, Zilberstein, Shlomo, Lesser, Victor
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	Washington, DC, USA IEEE Computer Society 19.07.2004 IEEE
Schriftenreihe:	ACM Conferences
Schlagworte:	Communication system control Computer science Computing methodologies > Artificial intelligence > Distributed artificial intelligence > Multi-agent systems Computing methodologies > Artificial intelligence > Planning and scheduling Distributed control Mathematics of computing > Probability and statistics > Probabilistic representations > Markov networks Mathematics of computing > Probability and statistics > Stochastic processes > Markov processes Multiagent systems Permission Power system modeling Power system planning Process planning State-space methods Stochastic 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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