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Argumentation as distributed constraint satisfaction: applications and results
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Source International Conference on Autonomous Agents archive
Proceedings of the fifth international conference on Autonomous agents table of contents
Montreal, Quebec, Canada
Pages: 324 - 331  
Year of Publication: 2001
ISBN:1-58113-326-X
Authors
Hyuckchul Jung  University of Southern California/Information Sciences Institute, 4676 Admiralty Way, Marina del Rey, CA
Milind Tambe  University of Southern California/Information Sciences Institute, 4676 Admiralty Way, Marina del Rey, CA
Shriniwas Kulkarni  University of Southern California/Information Sciences Institute, 4676 Admiralty Way, Marina del Rey, CA
Sponsor
SIGART: ACM Special Interest Group on Artificial Intelligence
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 9,   Downloads (12 Months): 41,   Citation Count: 15
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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.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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M. Tambe, Towards flexible teamwork, Journal of Artificial Intelligence Research (JAIR), 7:83-124, 1997.
 
13
M. Tambe and H. Jung, The benefits of arguing in a team, AI Magazine, 20(4), 1999.
 
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CITED BY  15

Collaborative Colleagues:
Hyuckchul Jung: colleagues
Milind Tambe: colleagues
Shriniwas Kulkarni: colleagues