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A survey of opponent modeling techniques in automated negotiation

A survey of opponent modeling techniques in automated negotiation
A survey of opponent modeling techniques in automated negotiation
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.
automated negotiation, learning techniques, machine learning, negotiation, opponent model, opponent modeling, software agents, survey
575-576
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) A survey of opponent modeling techniques in automated negotiation. AAMAS2016: 2016 International Conference on Autonomous Agents and Multi-agent Systems, Singapore. 09 - 13 May 2016. pp. 575-576 .

Record type: Conference or Workshop Item (Paper)

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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More information

e-pub ahead of print date: 2016
Venue - Dates: AAMAS2016: 2016 International Conference on Autonomous Agents and Multi-agent Systems, Singapore, 2016-05-09 - 2016-05-13
Keywords: automated negotiation, learning techniques, machine learning, negotiation, opponent model, opponent modeling, software agents, survey
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 393780
URI: https://eprints.soton.ac.uk/id/eprint/393780
PURE UUID: 3e882709-e3ab-4cea-9d61-e17a2dc7e27e
ORCID for Tim Baarslag: ORCID iD orcid.org/0000-0002-1662-3910

Catalogue record

Date deposited: 04 May 2016 11:18
Last modified: 06 Jun 2018 12:22

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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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