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ATAL
2003
Springer

Transition-independent decentralized markov decision processes

9 years 1 months ago
Transition-independent decentralized markov decision processes
There has been substantial progress with formal models for sequential decision making by individual agents using the Markov decision process (MDP). However, similar treatment of multi-agent systems is lacking. A recent complexity result, showing that solving decentralized MDPs is NEXPhard, provides a partial explanation. To overcome this complexity barrier, we identify a general class of transitionindependent decentralized MDPs that is widely applicable. The class consists of independent collaborating agents that are tied together through a global reward function that depends upon both of their histories. We present a novel algorithm for solving this class of problems and examine its properties. The result is the first effective technique to solve optimally a class of decentralized MDPs. This lays the foundation for further work in this area on both exact and approximate solutions. Categories and Subject Descriptors I.2.11 [Artificial Intelligence]: Distributed Artificial Intellig...
Raphen Becker, Shlomo Zilberstein, Victor R. Lesse
Added 06 Jul 2010
Updated 06 Jul 2010
Type Conference
Year 2003
Where ATAL
Authors Raphen Becker, Shlomo Zilberstein, Victor R. Lesser, Claudia V. Goldman
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