Article

Multi-agent learning model with bargaining

Authors:

Jerzy Rozenblit,

Ferenc Szidarovszky,

Lizhi YangAuthors Info & Claims

WSC '06: Proceedings of the 38th conference on Winter simulation

Pages 934 - 940

Published: 03 December 2006 Publication History

Abstract

Decision problems with the features of prisoner's dilemma are quite common. A general solution to this kind of social dilemma is that the agents cooperate to play a joint action. The Nash bargaining solution is an attractive approach to such cooperative games. In this paper, a multi-agent learning algorithm based on the Nash bargaining solution is presented. Different experiments are conducted on a testbed of stochastic games. The experimental results demonstrate that the algorithm converges to the policies of the Nash bargaining solution. Compared with the learning algorithms based on a non-cooperative equilibrium, this algorithm is fast and its complexity is linear with respect to the number of agents and number of iterations. In addition, it avoids the disturbing problem of equilibrium selection.

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Cited By

Crandall Jvan der Hoek WPadgham LConitzer VWinikoff M(2012)Just add PepperProceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 110.5555/2343576.2343633(399-406)Online publication date: 4-Jun-2012
https://dl.acm.org/doi/10.5555/2343576.2343633

Multi-agent learning model with bargaining
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence

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Published In

cover image ACM Conferences

WSC '06: Proceedings of the 38th conference on Winter simulation

December 2006

2429 pages

ISBN:1424405017

Editors:
L. Felipe Perrone
Bucknell University
,
Barry G. Lawson
University of Richmond
,
Jason Liu
Colorado School of Mines
,
Frederick P. Wieland
Sensis Corporation
,
General Chair:
David Nicol
University of Illinois, Urbana-Champaign
,
Program Chair:
Richard Fujimoto
College of Computing, Georgia Tech

Sponsors

IIE: Institute of Industrial Engineers
ASA: American Statistical Association
IEICE ESS: Institute of Electronics, Information and Communication Engineers, Engineering Sciences Society
IEEE-CS\DATC: The IEEE Computer Society
SIGSIM: ACM Special Interest Group on Simulation and Modeling
NIST: National Institute of Standards and Technology
(SCS): The Society for Modeling and Simulation International
INFORMS-CS: Institute for Operations Research and the Management Sciences-College on Simulation

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Winter Simulation Conference

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Published: 03 December 2006

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IIE
ASA
IEICE ESS
IEEE-CS\DATC
SIGSIM
NIST
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INFORMS-CS

WSC06: Winter Simulation Conference 2006

December 3 - 6, 2006

California, Monterey

Acceptance Rates

WSC '06 Paper Acceptance Rate 177 of 252 submissions, 70%;

Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

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Cited By

Crandall Jvan der Hoek WPadgham LConitzer VWinikoff M(2012)Just add PepperProceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 110.5555/2343576.2343633(399-406)Online publication date: 4-Jun-2012
https://dl.acm.org/doi/10.5555/2343576.2343633

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