Publication:
Distributed reinforcement learning in multi-agent decision systems

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ISSN: 0302-9743 (Print)
ISSN: 1611-3349 (Online)
ISBN: 978-3-540-64992-2 (Print)
ISBN: 978-3-540-49795-0 (Online)
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1998-01
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Springer
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Abstract
Decision problems can be usually solved using systems that implement different paradigms. These systems may be integrated into a single distributed system, with the expectation of obtaining a group performance more satisfactory than individual performances. Such a distributed system is what we call a Multi Agent Decision System (MADES), a special kind of Multi Agent System, that integrates several heterogeneous autonomous decision systems (agents). A MADES must produce a single solution proposal for the problem instance it faces, despite the fact that its decision making is distributed, and every agent produces solution proposals according to its local view and to its idiosyncrasy. We present a distributed reinforcement algorithm for learning how to combine the decisions the agents make in a distributed way, into a single group decision (solution proposal).
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Proceeding of: 6th Ibero-American Conference on AI (IBERAMIA '98),Lisbon, Portugal, October 5–9, 1998
Keywords
Multi agent systems, Machine learning, Distributed artificial intelligence
Bibliographic citation
Progress in artificial intelligence, Iberamia 98: 6th Ibero-American Conference on AI, Lisbon, Portugal, October 1998, p. 148-478