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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
Learning about the opponent in automated bilateral negotiation: a comprehensive survey of opponent modeling techniques
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 modelling, machine learning, survey
1387-2532
849-898
Baarslag, Tim
a7c541d8-8141-467b-a08c-7a81cd69920e
Hendrikx, Mark J.C.
6e034a9d-1885-48d9-9bbb-0288e5200e36
Hindriks, Koen V.
04b551cd-49a6-49cf-9c83-e1a19317eb0d
Jonker, Catholijn M.
b55441b1-d0fb-49f7-9a09-9375a9201fd0
Baarslag, Tim
a7c541d8-8141-467b-a08c-7a81cd69920e
Hendrikx, Mark J.C.
6e034a9d-1885-48d9-9bbb-0288e5200e36
Hindriks, Koen V.
04b551cd-49a6-49cf-9c83-e1a19317eb0d
Jonker, Catholijn M.
b55441b1-d0fb-49f7-9a09-9375a9201fd0

Baarslag, Tim, Hendrikx, Mark J.C., Hindriks, Koen V. and Jonker, Catholijn M. (2016) Learning about the opponent in automated bilateral negotiation: a comprehensive survey of opponent modeling techniques. Autonomous Agents and Multi-Agent Systems, 30 (5), 849-898. (doi:10.1007/s10458-015-9309-1).

Record type: Article

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.

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e-pub ahead of print date: 7 September 2016
Published date: September 2016
Keywords: negotiation, software agents, opponent model, learning techniques, automated negotiation, opponent modelling, machine learning, survey
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 381459
URI: https://eprints.soton.ac.uk/id/eprint/381459
ISSN: 1387-2532
PURE UUID: 216aa4dc-bc8a-4c9d-b2f6-b1c3bc36f1e9
ORCID for Tim Baarslag: ORCID iD orcid.org/0000-0002-1662-3910

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Date deposited: 06 Oct 2015 13:47
Last modified: 19 Jul 2019 20:33

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Contributors

Author: Tim Baarslag ORCID iD
Author: Mark J.C. Hendrikx
Author: Koen V. Hindriks
Author: Catholijn M. Jonker

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