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General principles of learning-based multi-agent systems (1999)
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| Venue: | In Proceedings of the Third International Conference of Autonomous Agents |
| Citations: | 38 - 7 self |
BibTeX
@INPROCEEDINGS{Wolpert99generalprinciples,
author = {David H. Wolpert},
title = {General principles of learning-based multi-agent systems},
booktitle = {In Proceedings of the Third International Conference of Autonomous Agents},
year = {1999},
pages = {77--83},
publisher = {ACM Press}
}
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Abstract
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 performance for our COINs in the leader-follower problem. 1
Keyphrases
general principle learning-based multi-agent system global goal leader-follower problem optimal performance core concept computer experiment real-world applicability coin perform reward function large decentralized multiagent system term artificial collective intelligence automated fashion embody solution bar problem mathematical framework difficult variant
