Learning about the opponent in automated bilateral negotiation: a comprehensive survey of opponent modeling techniques

Tim Baarslag*, Mark J.C. Hendrikx, Koen V. Hindriks, Catholijn M. Jonker

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

A negotiation between agents is typically an incomplete information game, where the agents initially do not know their opponent’s preferences or strategy. This poses a challenge, as efficient and effective negotiation requires the bidding agent to take the other’s wishes and future behavior into account when deciding on a proposal. Therefore, in order to reach better and earlier agreements, an agent can apply learning techniques to construct a model of the opponent. There is a mature body of research in negotiation that focuses on modeling the opponent, but there exists no recent survey of commonly used opponent modeling techniques. This work aims to advance and integrate knowledge of the field by providing a comprehensive survey of currently existing opponent models in a bilateral negotiation setting. We discuss all possible ways opponent modeling has been used to benefit agents so far, and we introduce a taxonomy of currently existing opponent models based on their underlying learning techniques. We also present techniques to measure the success of opponent models and provide guidelines for deciding on the appropriate performance measures for every opponent model type in our taxonomy.

Original languageEnglish
Pages (from-to)849-898
Number of pages50
JournalAutonomous Agents and Multi-Agent Systems
Volume30
Issue number5
DOIs
Publication statusPublished - 1 Sep 2016
Externally publishedYes

Keywords

  • Automated negotiation
  • Learning techniques
  • Machine learning
  • Negotiation
  • Opponent model
  • Opponent modeling
  • Software agents
  • Survey

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