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Learning opponents' preferences in multi-object automated negotiation

Published: 15 August 2005 Publication History

Abstract

We present a classification method for learning an opponent's preferences during a bilateral multi-issue negotiation. Similar candidate preference relations are grouped into classes, and a Bayesian technique is used to determine, for each class, the likelihood that the opponent's true preference relation over the set of offers lies in that class. Evidence used for classification decision-making is obtained by observing the opponents' sequence of offers, and applying the concession assumption, which states that negotiators usually decrease their offer utilities as time passes in order to find a deal. Simple experiments show that the technique can find the correct class after very few offers and can select a preference relation that is likely to match closely with the opponent's true preferences.

References

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Cited By

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  • (2021)A Comprehensive Review of Machine Learning in Multi-objective Optimization2021 IEEE 4th International Conference on Big Data and Artificial Intelligence (BDAI)10.1109/BDAI52447.2021.9515233(7-14)Online publication date: 2-Jul-2021
  • (2021)Agent Reasoning in AI-Powered NegotiationHandbook of Group Decision and Negotiation10.1007/978-3-030-49629-6_26(1187-1211)Online publication date: 18-May-2021
  • (2020)Agent Reasoning in AI-Powered NegotiationHandbook of Group Decision and Negotiation10.1007/978-3-030-12051-1_26-1(1-25)Online publication date: 19-Oct-2020
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Published In

cover image ACM Other conferences
ICEC '05: Proceedings of the 7th international conference on Electronic commerce
August 2005
957 pages
ISBN:1595931120
DOI:10.1145/1089551
  • Conference Chairs:
  • Qi Li,
  • Ting-Peng Liang
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 August 2005

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Author Tags

  1. Bayesian classification
  2. automated negotiation
  3. multi-issue
  4. preference elicitation
  5. utility

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Cited By

View all
  • (2021)A Comprehensive Review of Machine Learning in Multi-objective Optimization2021 IEEE 4th International Conference on Big Data and Artificial Intelligence (BDAI)10.1109/BDAI52447.2021.9515233(7-14)Online publication date: 2-Jul-2021
  • (2021)Agent Reasoning in AI-Powered NegotiationHandbook of Group Decision and Negotiation10.1007/978-3-030-49629-6_26(1187-1211)Online publication date: 18-May-2021
  • (2020)Agent Reasoning in AI-Powered NegotiationHandbook of Group Decision and Negotiation10.1007/978-3-030-12051-1_26-1(1-25)Online publication date: 19-Oct-2020
  • (2017)Rethinking Frequency Opponent Modeling in Automated NegotiationPRIMA 2017: Principles and Practice of Multi-Agent Systems10.1007/978-3-319-69131-2_16(263-279)Online publication date: 5-Oct-2017
  • (2016)Automated Multilateral Negotiation on Multiple Issues with Private InformationINFORMS Journal on Computing10.1287/ijoc.2016.070128:4(612-628)Online publication date: Nov-2016
  • (2016)Learning about the opponent in automated bilateral negotiationAutonomous Agents and Multi-Agent Systems10.1007/s10458-015-9309-130:5(849-898)Online publication date: 1-Sep-2016
  • (2016)Predicting the Performance of Opponent ModelsExploring the Strategy Space of Negotiating Agents10.1007/978-3-319-28243-5_7(129-146)Online publication date: 22-Jan-2016
  • (2016)BackgroundExploring the Strategy Space of Negotiating Agents10.1007/978-3-319-28243-5_2(15-52)Online publication date: 22-Jan-2016
  • (2014)Decentralized Strategy for Supporting Multi-agent Negotiation of Several Aspects of Different Products2014 33rd International Conference of the Chilean Computer Science Society (SCCC)10.1109/SCCC.2014.29(34-38)Online publication date: Nov-2014
  • (2014)Strategies for avoiding preference profiling in agent-based e-commerce environmentsApplied Intelligence10.1007/s10489-013-0448-240:1(127-142)Online publication date: 1-Jan-2014
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