Negotiating with unknown opponents Toward multi-lateral agreement in real-time domains

Siqi Chen, J. Hao, Shuang Zhou, Gerhard Weiss

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Automated negotiation has been gained a mass of attention mainly because of its broad application potential in many fields. This work studies a prominent class of automated negotiations – multi-lateral multi-issue negotiations under real-time constraints, where the negotiation agents are given no prior information about their opponents’ preferences over the negotiation outcome space. A novel negotiation approach is proposed that enables an agent to obtain efficient agreements in this challenging multi-lateral negotiations. The proposed approach achieves that goal by, (1) employing sparse pseudo-input Gaussian processes (SPGPs) to model opponents, (2) learning fuzzy opponent preferences to increase the satisfaction of other parties, and (3) adopting an adaptive decision-making mechanism to handle uncertainty in negotiation.
Original languageEnglish
Title of host publicationModern Approaches to Agent-based Complex Automated Negotiation
EditorsK. Fuijta, Q. Bai, T. Ito, M. Zhang, F. Ren, R. Aydogan, R. Hadfi
PublisherSpringer Verlag
Number of pages11
ISBN (Electronic)978-3-319-51563-2
ISBN (Print)978-3-319-51561-8
Publication statusPublished - 2017

Publication series

SeriesStudies in Computational Intelligence

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