Article

The Benefits of Opponent Models in Negotiation

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WI-IAT '09: Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02

Pages 439 - 444

https://doi.org/10.1109/WI-IAT.2009.192

Published: 15 September 2009 Publication History

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Abstract

Information about the opponent is essential to improve automated negotiation strategies for bilateral multi-issue negotiation. In this paper we propose a negotiation strategy that exploits a technique to learn a model of opponent preferences in a single negotiation session. An opponent model may be used to achieve at least two important goals in negotiation. First, it can be used to recognize, avoid and respond appropriately to exploitation, which differentiates the strategy proposed from commonly used concession-based strategies. Second, it can be used to increase the efficiency of a negotiated agreement by searching for Pareto-optimal bids. A negotiation strategy should be efficient, transparent, maximize the chance of an agreement and should avoid exploitation. We argue that the proposed strategy satisfies these criteria and analyze its performance experimentally.

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Cited By

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Kröhling DChiotti OMartínez E(2023)Artificial Theory of Mind in contextual automated negotiations within peer-to-peer marketsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.105887120:COnline publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.105887
Keskin MBuzcu BAydoğan R(2023)Conflict-based negotiation strategy for human-agent negotiationApplied Intelligence10.1007/s10489-023-05001-953:24(29741-29757)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s10489-023-05001-9
Alrayes BKafalı ÖStathis K(2018)Concurrent bilateral negotiation for open e-marketsKnowledge and Information Systems10.1007/s10115-017-1125-256:2(463-501)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.1007/s10115-017-1125-2
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Index Terms

The Benefits of Opponent Models in Negotiation
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Cooperation and coordination
      2. Multi-agent systems
    2. Planning and scheduling
  2. Modeling and simulation
    1. Model development and analysis
      1. Model verification and validation
      2. Modeling methodologies

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WI-IAT '09: Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02

September 2009

601 pages

ISBN:9780769538013

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IEEE Computer Society

United States

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Published: 15 September 2009

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Kröhling DChiotti OMartínez E(2023)Artificial Theory of Mind in contextual automated negotiations within peer-to-peer marketsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.105887120:COnline publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.105887
Keskin MBuzcu BAydoğan R(2023)Conflict-based negotiation strategy for human-agent negotiationApplied Intelligence10.1007/s10489-023-05001-953:24(29741-29757)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s10489-023-05001-9
Alrayes BKafalı ÖStathis K(2018)Concurrent bilateral negotiation for open e-marketsKnowledge and Information Systems10.1007/s10115-017-1125-256:2(463-501)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.1007/s10115-017-1125-2
Haberland VMiles SLuck M(2017)Negotiation strategy for continuous long-term tasks in a grid environmentAutonomous Agents and Multi-Agent Systems10.1007/s10458-015-9316-231:1(130-150)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1007/s10458-015-9316-2
Baarslag THendrikx MHindriks KJonker C(2016)Learning about the opponent in automated bilateral negotiationAutonomous Agents and Multi-Agent Systems10.1007/s10458-015-9309-130:5(849-898)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1007/s10458-015-9309-1
Chen SWeiss G(2014)An Intelligent Agent for Bilateral Negotiation with Unknown Opponents in Continuous-Time DomainsACM Transactions on Autonomous and Adaptive Systems10.1145/26295779:3(1-24)Online publication date: 7-Oct-2014
https://dl.acm.org/doi/10.1145/2629577
Marsa-Maestre IKlein MJonker CAydoğan R(2014)From problems to protocolsDecision Support Systems10.1016/j.dss.2013.05.01960(39-54)Online publication date: 1-Apr-2014
https://dl.acm.org/doi/10.1016/j.dss.2013.05.019
Baarslag TFujita KGerding EHindriks KIto TJennings NJonker CKraus SLin RRobu VWilliams C(2013)Evaluating practical negotiating agentsArtificial Intelligence10.1016/j.artint.2012.09.004198(73-103)Online publication date: 1-May-2013
https://dl.acm.org/doi/10.1016/j.artint.2012.09.004

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Opponent modelling in automated multi-issue negotiation using Bayesian learning

Using opponent models for efficient negotiation

Bayesian-based preference prediction in bilateral multi-issue negotiation between intelligent agents

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Opponent modelling in automated multi-issue negotiation using Bayesian learning

Using opponent models for efficient negotiation

Bayesian-based preference prediction in bilateral multi-issue negotiation between intelligent agents

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