Unanimously Acceptable Agreements for Negotiation Teams in Unpredictable Domains

Victor Sanchez-Anguix, Reyhan Aydogan, Vicente Julian, Catholijn Jonker

Research output: Contribution to journalArticle

12 Citations (Scopus)
7 Downloads (Pure)

Abstract

A negotiation team is a set of agents with common and possibly also conflicting preferences that forms one of the parties of a negotiation. A negotiation team is involved in two decision making processes simultaneously, a negotiation with the opponents, and an intra-team process to decide on the moves to make in the negotiation. This article focuses on negotiation team decision making for circumstances that require unanimity of team decisions. Existing agent-based approaches only guarantee unanimity in teams negotiating in domains exclusively composed of predictable and compatible issues. This article presents a model for negotiation teams that guarantees unanimous team decisions in domains consisting of predictable and compatible, and also unpredictable issues. Moreover, the article explores the influence of using opponent, and team member models in the proposing strategies that team members use. Experimental results show that the team benefits if team members employ Bayesian learning to model their teammates’ preferences.

Publisher Statement: NOTICE: this is the author’s version of a work that was accepted for publication in Electronic Commerce Research and Applications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Electronic Commerce Research and Applications, [13, 4, (2014)] DOI: 10.1016/j.elerap.2014.05.002

© 2014, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Original languageEnglish
Pages (from-to)243-265
Number of pages23
JournalElectronic Commerce Research and Applications
Volume13
Issue number4
Early online date28 May 2014
DOIs
Publication statusPublished - Jul 2014
Externally publishedYes

Bibliographical note

JCR Science Impact Factor 1.480, Computer Science (Artificial Intelligence) Q2

Keywords

  • Automated negotiation
  • Multi-agent systems
  • Agreement technologies

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