Emerging coordination in infinite team Markov games

In this paper we address the problem of coordination in multi-agent sequential decision problems with infinite statespaces. We adopt a game theoretic formalism to describe the interaction of the multiple decision-makers and propose the novel approximate biased adaptive play algorithm. This algorithm is an extension of biased adaptive play to team Markov games defined over infinite state-spaces. We establish our method to coordinate with probability 1 in the optimal strategy and discuss how this methodology can be combined with approximate learning architectures. We conclude with two simple examples of application of our algorithm. Categories and Subject Descriptors I.2.11 [Artificial Intelligence]: Distributed Artificial Intelligence--Multiagent systems, Coherence and coordination General Terms Algorithms, Theory Keywords Team Markov games, coordination, biased adaptive play

• Type: Conference
• Year: 2008
• Where: ATAL
• Authors: Francisco S. Melo, M. Isabel Ribeiro
• Keywords: Adaptive Play, Adaptive Play Algorithm, ATAL 2008, Intelligent Agents, Markov Games