Predicting the performance of opponent models in automated negotiation

Tim Baarslag, Mark Hendrikx, Koen Hindriks, Catholijn Jonker

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

Abstract

When two agents settle a mutual concern by negotiating with each other, they usually do not share their preferences so as to avoid exploitation. In such a setting, the agents may need to analyze each other's behavior to make an estimation of the opponent's preferences. This process of opponent modeling makes it possible to find a satisfying negotiation outcome for both parties. A large number of such opponent modeling techniques have already been introduced, together with different measures to assess their quality. The quality of an opponent model can be measured in two different ways: one is to use the agent's performance as a benchmark for the model's quality, the other is to directly evaluate its accuracy by using similarity measures. Both methods have been used extensively, and both have their distinct advantages and drawbacks. In this work we investigate the exact relation between the two, and we pinpoint the measures for accuracy that best predict performance gain. This leads us to new insights in how to construct an opponent model, and what we need to measure when optimizing performance.

Original languageEnglish
Title of host publicationProceedings - 2013 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2013
PublisherACM, IEEE Computer Society
Pages59-66
Number of pages8
Volume2
ISBN (Print)9781479929023
DOIs
Publication statusPublished - 1 Jan 2013
Externally publishedYes
Event2013 12th IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2013 - Atlanta, GA, United States
Duration: 17 Nov 201320 Nov 2013

Conference

Conference2013 12th IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2013
CountryUnited States
CityAtlanta, GA
Period17/11/1320/11/13

Keywords

  • Intelligent agents
  • Machine learning
  • Multiagent systems

Cite this

Baarslag, T., Hendrikx, M., Hindriks, K., & Jonker, C. (2013). Predicting the performance of opponent models in automated negotiation. In Proceedings - 2013 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2013 (Vol. 2, pp. 59-66). [6690771] ACM, IEEE Computer Society. https://doi.org/10.1109/WI-IAT.2013.91