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

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

Baarslag, T.
Hendrikx, M.J.C.
Hindriks, K.V.
Jonker, C.M.

Electrical Engineering, Mathematics and Computer Science

Intelligent Systems

2015-09-07

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.

negotiation
software agents
opponent model
learning techniques
automated negotiation
opponent modeling
machine learning
survey

http://resolver.tudelft.nl/uuid:785553f9-d31d-4600-a5bd-2f4cd6ab2132

Springer

1387-2532

https://doi.org/10.1007/s10458-015-9309-1

Autonomous Agents and Multi-Agent Systems, 2015

Institutional Repository

journal article

(c) 2015 The Author(s)
This article is published with open access at Springerlink.com