ANALYZING MYOPIC APPROACHES FOR MULTI-AGENT COMMUNICATION

Author:

Becker Raphen,Carlin Alan,Lesser Victor,Zilberstein Shlomo

Publisher

Wiley

Subject

Artificial Intelligence,Computational Mathematics

Reference26 articles.

1. Solving transition independent decentralized Markov decision processes;Becker;Journal of Artificial Intelligence Research,2004

2. The complexity of decentralized control of Markov decision processes;Bernstein;Mathematics of Operations Research,2002

3. Boutilier, C. 1999. Sequential optimality and coordination in multiagent systems. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, pp. 478-485.

4. Emery-Montemerlo, R. , G. Gordon , J. Schneider , and S. Thrun . 2004. Approximate solutions for partially observable stochastic games with common payoffs. In Proceedings of the Third International Joint Conference on Autonomous Agents and Multi-Agent Systems. ACM Press, New York, pp. 136-143.

5. Ghavamzadeh, M. , and S. Mahadevan . 2004. Learning to communicate and act in cooperative multiagent systems using hierarchical reinforcement learning. In Proceedings of the Third International Joint Conference on Autonomous Agents and Multi-Agent Systems. ACM Press, New York, pp. 1114-1121.

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