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Measuring the performance of online opponent models in automated bilateral negotiation

Measuring the performance of online opponent models in automated bilateral negotiation
Measuring the performance of online opponent models in automated bilateral negotiation
n important aim in bilateral negotiations is to achieve a win-win solution for both parties; therefore, a critical aspect of a negotiating agent’s success is its ability to take the opponent’s preferences into account. Every year, new negotiation agents are introduced with better learning techniques to model the opponent. Our main goal in this work is to evaluate and compare the performance of a selection of state-of-the-art online opponent modeling techniques in negotiation, and to determine under which circumstances they are beneficial in a real-time, online negotiation setting. Towards this end, we provide an overview of the factors influencing the quality of a model and we analyze how the performance of opponent models depends on the negotiation setting. This results in better insight into the performance of opponent models, and allows us to pinpoint well-performing opponent modeling techniques that did not receive much previous attention in literature.
negotiation, opponent model performance, quality measures
978-3-642-35100-6
0302-9743
1-14
Springer Berlin Heidelberg
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
Thielscher, Michael
Zhang, Dongmo
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
Thielscher, Michael
Zhang, Dongmo

Baarslag, Tim, Hendrikx, Mark, Hindriks, Koen and Jonker, Catholijn (2012) Measuring the performance of online opponent models in automated bilateral negotiation. Thielscher, Michael and Zhang, Dongmo (eds.) In AI 2012: Advances in Artificial Intelligence. vol. 7691, Springer Berlin Heidelberg. pp. 1-14 . (doi:10.1007/978-3-642-35101-3_1).

Record type: Conference or Workshop Item (Paper)

Abstract

n important aim in bilateral negotiations is to achieve a win-win solution for both parties; therefore, a critical aspect of a negotiating agent’s success is its ability to take the opponent’s preferences into account. Every year, new negotiation agents are introduced with better learning techniques to model the opponent. Our main goal in this work is to evaluate and compare the performance of a selection of state-of-the-art online opponent modeling techniques in negotiation, and to determine under which circumstances they are beneficial in a real-time, online negotiation setting. Towards this end, we provide an overview of the factors influencing the quality of a model and we analyze how the performance of opponent models depends on the negotiation setting. This results in better insight into the performance of opponent models, and allows us to pinpoint well-performing opponent modeling techniques that did not receive much previous attention in literature.

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More information

Published date: 2012
Venue - Dates: conference; 2012-01-01, 2012-01-01
Keywords: negotiation, opponent model performance, quality measures
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 373624
URI: https://eprints.soton.ac.uk/id/eprint/373624
ISBN: 978-3-642-35100-6
ISSN: 0302-9743
PURE UUID: 92b4d56b-a282-4643-8ac2-c4911580790f
ORCID for Tim Baarslag: ORCID iD orcid.org/0000-0002-1662-3910

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Date deposited: 27 Jan 2015 14:16
Last modified: 06 Jun 2018 12:22

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