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AGENTS
1999
Springer

General Principles of Learning-Based Multi-Agent Systems

8 years 7 months ago
General Principles of Learning-Based Multi-Agent Systems
We consider the problem of how to design large decentralized multiagent 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 [1] (and in particular avoid the tragedy of the commons for that problem), and we also illustrate optimal performanc...
David Wolpert, Kevin R. Wheeler, Kagan Tumer
Added 03 Aug 2010
Updated 03 Aug 2010
Type Conference
Year 1999
Where AGENTS
Authors David Wolpert, Kevin R. Wheeler, Kagan Tumer
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