An Efficient Multi-Attribute Negotiation System using Learning Agents for Reciprocity 


Vol. 11,  No. 3, pp. 731-740, Jun.  2004
10.3745/KIPSTD.2004.11.3.731


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  Abstract

In this paper we propose a fast negotiation agent system that guarantees the reciprocity of the attendants in a bilateral negotiation on the e-commerce. The proposednegotiation agent system exploits the incremental learning method based on an artificial neural network in generating a counter-offer and is trained by the previous offer that has been rejected by the other party. During a negotiation, the software agents on behalf of a buyer and a seller negotiate each other by considering the multi-attributes of a product. The experimental results show that the proposed negotiation system achieves better agreements than other negotiation agent systems that are operated under the realistic and practical environment. Furthermore, the proposed system carries out negotiations about twenty times faster than the previous negotiation systems on the average.

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  Cite this article

[IEEE Style]

S. H. Park and S. B. Yang, "An Efficient Multi-Attribute Negotiation System using Learning Agents for Reciprocity," The KIPS Transactions:PartD, vol. 11, no. 3, pp. 731-740, 2004. DOI: 10.3745/KIPSTD.2004.11.3.731.

[ACM Style]

Sang Hyun Park and Sung Bong Yang. 2004. An Efficient Multi-Attribute Negotiation System using Learning Agents for Reciprocity. The KIPS Transactions:PartD, 11, 3, (2004), 731-740. DOI: 10.3745/KIPSTD.2004.11.3.731.