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General Principles of Learning-Based Multi-Agent Systems | David H. Wolpert
; Kevin R. Wheeler
; Kagan Tumer
; | Date: |
11 May 1999 | Journal: | Proceedings of the Third International Conference on Autonomous Agents, Seatle, WA 1999 | Subject: | Multiagent Systems; Distributed, Parallel, and Cluster Computing; Learning; Statistical Mechanics; Adaptation and Self-Organizing Systems ACM-class: I.2.6 ; I.2.11 | cs.MA adap-org cond-mat.stat-mech cs.DC cs.LG nlin.AO | Abstract: | We consider the problem of how to design large decentralized multi-agent systems (MAS’s) in an automated fashion, with little or no hand-tuning. Our approach has each agent run a reinforcement learning algorithm. This converts the problem into one of how to automatically set/update the reward functions for each of the agents so that the global goal is achieved. In particular we do not want the agents to ``work at cross-purposes’’ as far as the global goal is concerned. We use the term artificial COllective INtelligence (COIN) to refer to systems that embody solutions to this problem. In this paper we present a summary of a mathematical framework for COINs. We then investigate the real-world applicability of the core concepts of that framework via two computer experiments: we show that our COINs perform near optimally in a difficult variant of Arthur’s bar problem (and in particular avoid the tragedy of the commons for that problem), and we also illustrate optimal performance for our COINs in the leader-follower problem. | Source: | arXiv, cs.MA/9905005 | Services: | Forum | Review | PDF | Favorites |
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