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

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

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic

Abstract

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
PublisherSpringer Verlag
Pages219-229
Number of pages11
Publication statusPublished - 2017

Publication series

SeriesStudies in Computational Intelligence
Volume674

Cite this

Chen, S., Hao, J., Zhou, S., & Weiss, G. (2017). Negotiating with unknown opponents: Toward multi-lateral agreement in real-time domains. In K. Fuijta (Ed.), Modern Approaches to Agent-based Complex Automated Negotiation (pp. 219-229). Springer Verlag. Studies in Computational Intelligence, Vol.. 674
Chen, Siqi ; Hao, J. ; Zhou, Shuang ; Weiss, Gerhard. / Negotiating with unknown opponents : Toward multi-lateral agreement in real-time domains. Modern Approaches to Agent-based Complex Automated Negotiation. editor / K. Fuijta. Springer Verlag, 2017. pp. 219-229 (Studies in Computational Intelligence, Vol. 674).
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abstract = "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.",
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year = "2017",
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series = "Studies in Computational Intelligence",
pages = "219--229",
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Chen, S, Hao, J, Zhou, S & Weiss, G 2017, Negotiating with unknown opponents: Toward multi-lateral agreement in real-time domains. in K Fuijta (ed.), Modern Approaches to Agent-based Complex Automated Negotiation. Springer Verlag, Studies in Computational Intelligence, vol. 674, pp. 219-229.

Negotiating with unknown opponents : Toward multi-lateral agreement in real-time domains. / Chen, Siqi; Hao, J.; Zhou, Shuang; Weiss, Gerhard.

Modern Approaches to Agent-based Complex Automated Negotiation. ed. / K. Fuijta. Springer Verlag, 2017. p. 219-229 (Studies in Computational Intelligence, Vol. 674).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic

TY - CHAP

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PY - 2017

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N2 - 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.

AB - 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.

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BT - Modern Approaches to Agent-based Complex Automated Negotiation

A2 - Fuijta, K.

PB - Springer Verlag

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Chen S, Hao J, Zhou S, Weiss G. Negotiating with unknown opponents: Toward multi-lateral agreement in real-time domains. In Fuijta K, editor, Modern Approaches to Agent-based Complex Automated Negotiation. Springer Verlag. 2017. p. 219-229. (Studies in Computational Intelligence, Vol. 674).