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Argumentation as distributed constraint satisfaction: applications and results

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Published:28 May 2001Publication History

ABSTRACT

Conflict resolution is a critical problem in distributed and collaborative multi-agent systems. Negotiation via argumentation (NVA), where agents provide explicit arguments or justifications for their proposals for resolving conflicts, is an effective approach to resolve conflicts. Indeed, we are applying argumentation in some real-world multi-agent applications. However, a key problem in such applications is that a well-understood computational model of argumentation is currently missing, making it difficult to investigate convergence and scalability of argumentation techniques, and to understand and characterize different collaborative NVA strategies in a principled manner. To alleviate these difficulties, we present distributed constraint satisfaction problem (DCSP) as a computational model for investigating NVA. We model argumentation as constraint propagation in DCSP. This model enables us to study convergence properties of argumentation, and formulate and experimentally compare 16 different NVA strategies with different levels of agent cooperativeness towards others. One surprising result from our experiments is that maximizing cooperativeness is not necessarily the best strategy even in a completely cooperative environment. The paper illustrates the usefulness of these results in applying NVA to multi-agent systems, as well as to DCSP systems in general.

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  1. Argumentation as distributed constraint satisfaction: applications and results

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      • Published in

        cover image ACM Conferences
        AGENTS '01: Proceedings of the fifth international conference on Autonomous agents
        May 2001
        662 pages
        ISBN:158113326X
        DOI:10.1145/375735

        Copyright © 2001 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 28 May 2001

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        AGENTS '01 Paper Acceptance Rate66of248submissions,27%Overall Acceptance Rate182of599submissions,30%

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