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2008
Springer

Interaction-driven Markov games for decentralized multiagent planning under uncertainty

8 years 4 months ago
Interaction-driven Markov games for decentralized multiagent planning under uncertainty
In this paper we propose interaction-driven Markov games (IDMGs), a new model for multiagent decision making under uncertainty. IDMGs aim at describing multiagent decision problems in which interaction among agents is a local phenomenon. To this purpose, we explicitly distinguish between situations in which agents should interact and situations in which they can afford to act independently. The agents are coupled through the joint rewards and joint transitions in the states in which they interact. The model combines several fundamental properties from transition-independent Dec-MDPs and weakly coupled MDPs while allowing to address, in several aspects, more general problems. We introduce a fast approximate solution method for planning in IDMGs, exploiting their particular structure, and we illustrate its successful application on several large multiagent tasks. Categories and Subject Descriptors I.2.11 [Artificial Intelligence]: Distributed Artificial Intelligence--Multiagent systems ...
Matthijs T. J. Spaan, Francisco S. Melo
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2008
Where ATAL
Authors Matthijs T. J. Spaan, Francisco S. Melo
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