Using opponent models for efficient negotiation

Koen Hindriks, Catholijn Jonker, Dmytro Tykhonov

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

Information about the opponent is essential to improve automated negotiation strategies for bilateral multi-issue negotiation. In this paper we propose a negotiation strategy that combines a Bayesian technique to learn the preferences of an opponent during bidding and a Tit-for-Tat-like strategy to avoid exploitation by the opponent. The learned opponent model is used to achieve two important goals in negotiation. It may be used to increase the efficiency of negotiation by searching for Pareto optimal bids and to avoid exploitation by making moves that mirror the move of the other party. The performance of the proposed negotiation strategy is analyzed in a tournament setup.

Original languageEnglish
Title of host publication8th International Joint Conference on Autonomous Agents and Multiagent Systems 2009, AAMAS 2009
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages1238-1239
Number of pages2
Volume2
ISBN (Print)9781615673346
Publication statusPublished - 1 Jan 2009
Externally publishedYes
Event8th International Joint Conference on Autonomous Agents and Multiagent Systems 2009, AAMAS 2009 - Budapest, Hungary
Duration: 10 May 200915 May 2009

Conference

Conference8th International Joint Conference on Autonomous Agents and Multiagent Systems 2009, AAMAS 2009
Country/TerritoryHungary
CityBudapest
Period10/05/0915/05/09

Keywords

  • Automated multi-issue negotiation
  • Bayesian learning
  • Negotiation strategy
  • Opponent modelling
  • Tit-for-tat

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