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Predicting the performance of opponent models in automated negotiation

Predicting the performance of opponent models in automated negotiation
Predicting the performance of opponent models in automated negotiation
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
intelligent agents, machine learning, multiagent systems
59-66
Baarslag, Tim
a7c541d8-8141-467b-a08c-7a81cd69920e
Hendrikx, Mark
9b2ccb11-28b0-4b28-8334-15b03423c12b
Hindriks, Koen
37537aff-8c5e-420e-b424-1cb0c26aa7d7
Jonker, Catholijn
492a7c03-c206-4fad-9a9c-a156a96c4245
Baarslag, Tim
a7c541d8-8141-467b-a08c-7a81cd69920e
Hendrikx, Mark
9b2ccb11-28b0-4b28-8334-15b03423c12b
Hindriks, Koen
37537aff-8c5e-420e-b424-1cb0c26aa7d7
Jonker, Catholijn
492a7c03-c206-4fad-9a9c-a156a96c4245

Baarslag, Tim, Hendrikx, Mark, Hindriks, Koen and Jonker, Catholijn (2013) Predicting the performance of opponent models in automated negotiation. Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2013 IEEE/WIC/ACM International Joint Conferences on, United States. 17 - 20 Nov 2013. pp. 59-66 .

Record type: Conference or Workshop Item (Paper)

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

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More information

Published date: November 2013
Venue - Dates: Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2013 IEEE/WIC/ACM International Joint Conferences on, United States, 2013-11-17 - 2013-11-20
Keywords: intelligent agents, machine learning, multiagent systems
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 373650
URI: https://eprints.soton.ac.uk/id/eprint/373650
PURE UUID: 8220bbf8-ddab-4919-a1fd-99356365be2e
ORCID for Tim Baarslag: ORCID iD orcid.org/0000-0002-1662-3910

Catalogue record

Date deposited: 26 Jan 2015 11:42
Last modified: 06 Jun 2018 12:22

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