Measuring the performance of online opponent models in automated bilateral negotiation

Tim Baarslag*, Mark Hendrikx, Koen Hindriks, Catholijn Jonker

*Corresponding author for this work

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


An important aim in bilateral negotiations is to achieve a win-win solution for both parties; therefore, a critical aspect of a negotiating agent's success is its ability to take the opponent's preferences into account. Every year, new negotiation agents are introduced with better learning techniques to model the opponent. Our main goal in this work is to evaluate and compare the performance of a selection of state-of-the-art online opponent modeling techniques in negotiation, and to determine under which circumstances they are beneficial in a real-time, online negotiation setting. Towards this end, we provide an overview of the factors influencing the quality of a model and we analyze how the performance of opponent models depends on the negotiation setting. This results in better insight into the performance of opponent models, and allows us to pinpoint well-performing opponent modeling techniques that did not receive much previous attention in literature.

Original languageEnglish
Title of host publicationAI 2012
Subtitle of host publicationAdvances in Artificial Intelligence - 25th Australasian Joint Conference, Proceedings
Number of pages14
Publication statusPublished - 26 Dec 2012
Externally publishedYes
Event25th Australasian Joint Conference on Artificial Intelligence, AI 2012 - Sydney, NSW, Australia
Duration: 4 Dec 20127 Dec 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7691 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference25th Australasian Joint Conference on Artificial Intelligence, AI 2012
CitySydney, NSW


  • Negotiation
  • Opponent Model Performance
  • Quality Measures


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