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The Convergence of TD(λ) for General λ
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An adaptive communication protocol for cooperating mobile robots
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Acting optimally in partially observable stochastic domains
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Control strategies for a stochastic planner
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Multiagent Coordination with Learning Classifier Systems
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Probabilistic robot navigation in partially observable environments
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Process-oriented planning and average-reward optimality
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Exploiting structure in policy construction
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Decomposition techniques for planning in stochastic domains
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The dynamics of reinforcement learning in cooperative multiagent systems
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UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
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There has been a growing interest in AI in the design of multiagent systems, especially in multiagent cooperative planning. In this paper, we investigate the extent to which methods from single-agent planning and learning can be applied in multiagent settings. We survey a number of different techniques from decision-theoretic planning and reinforcement learning and describe a number of interesting issues that arise with regard to coordinating the policies of individual agents. To this end, we describe multiagent Markov decision processes as a general model in which to frame this discussion. These are special n-person cooperative games in which agents share the same utility function. We discuss coordination mechanisms based on imposed conventions (or social laws) as well as learning methods for coordination. Our focus is on the decomposition of sequential decision processes so that coordination can be learned (or imposed) locally, at the level of individual states. We also discuss the use of structured problem representations and their role in the generalization of learned conventions and in approximation.