Article

A game tree strategy for automated negotiation

Authors:

Alan H. Karp,

R. Wu,

K-Y Chen,

A. ZhangAuthors Info & Claims

EC '04: Proceedings of the 5th ACM conference on Electronic commerce

Pages 228 - 229

https://doi.org/10.1145/988772.988807

Published: 17 May 2004 Publication History

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Abstract

Chess playing programs running on small computers, such as PocketPCs,can beat most human players. This paper reports a feasibility studyto determine if the techniques programs use to play chess can beapplied to the more economically interesting problem of negotiation.This study allowed us to identify the essential differences betweenplaying chess and negotiating and to demonstrate possible solutions tothe problems we encountered.

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[1]

A. H. Karp, R. Wu, K.-Y. Chen, and A. Zhang. A game tree strategy for automated negotiation. http://www.hpl.hp.com/techreports/2003/HPL-2003-154.html.

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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
Baarslag TBaarslag T(2016)BackgroundExploring the Strategy Space of Negotiating Agents10.1007/978-3-319-28243-5_2(15-52)Online publication date: 22-Jan-2016
https://doi.org/10.1007/978-3-319-28243-5_2
Baarslag TBaarslag T(2016)IntroductionExploring the Strategy Space of Negotiating Agents10.1007/978-3-319-28243-5_1(1-14)Online publication date: 22-Jan-2016
https://doi.org/10.1007/978-3-319-28243-5_1
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A game tree strategy for automated negotiation

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An agent engaged in multi-issue automated negotiation can benefit greatly from learning about its opponent's preferences. Knowledge of the opponent's preferences can help the agent not only to find mutually acceptable agreements more quickly, but also ...

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Published In

EC '04: Proceedings of the 5th ACM conference on Electronic commerce

May 2004

278 pages

ISBN:1581137710

DOI:10.1145/988772

General Chair:
Jack Breese
Microsoft
,
Program Chairs:
Joan Feigenbaum
Yale University
,
Margo Seltzer
Harvard University

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 May 2004

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automated negotiation

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EC04

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EC04: Fifth ACM Conference on Electronic Commerce 2004 ( held in conjunction with WWW'04: The 2004 World Wide Web Conference )

May 17 - 20, 2004

NY, New York, USA

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EC '04 Paper Acceptance Rate 24 of 146 submissions, 16%;

Overall Acceptance Rate 664 of 2,389 submissions, 28%

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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
Baarslag TBaarslag T(2016)BackgroundExploring the Strategy Space of Negotiating Agents10.1007/978-3-319-28243-5_2(15-52)Online publication date: 22-Jan-2016
https://doi.org/10.1007/978-3-319-28243-5_2
Baarslag TBaarslag T(2016)IntroductionExploring the Strategy Space of Negotiating Agents10.1007/978-3-319-28243-5_1(1-14)Online publication date: 22-Jan-2016
https://doi.org/10.1007/978-3-319-28243-5_1
Lin RKraus SBaarslag TTykhonov DHindriks KJonker C(2014)GENIUSComputational Intelligence10.1111/j.1467-8640.2012.00463.x30:1(48-70)Online publication date: 1-Feb-2014
https://dl.acm.org/doi/10.1111/j.1467-8640.2012.00463.x
Lin RKraus STykhonov DHindriks KJonker C(2010)Supporting the Design of General Automated NegotiatorsInnovations in Agent-Based Complex Automated Negotiations10.1007/978-3-642-15612-0_4(69-87)Online publication date: 2010
https://doi.org/10.1007/978-3-642-15612-0_4
Resinas MFernandez PCorchuelo R(2008)Towards Automated Service TradingE-Business and Telecommunication Networks10.1007/978-3-540-70760-8_1(3-14)Online publication date: 2008
https://doi.org/10.1007/978-3-540-70760-8_1
Resinas MFernandez PCorchuelo R(2006)A conceptual framework for automated negotiation systemsProceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning10.1007/11875581_148(1250-1258)Online publication date: 20-Sep-2006
https://dl.acm.org/doi/10.1007/11875581_148
Karp A(2004)Rules of engagement for automated negotiationProceedings. First IEEE International Workshop on Electronic Contracting, 2004.10.1109/WEC.2004.1319506(32-39)Online publication date: 2004
https://doi.org/10.1109/WEC.2004.1319506

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Learning about the opponent in automated bilateral negotiation: a comprehensive survey of opponent modeling techniques

Optimal time-based strategy for automated negotiation

Detecting opponent concessions in multi-issue automated negotiation

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