An evolutionary model of multi-agent learning with a varying exploration rate

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

An evolutionary model of multi-agent learning with a varying exploration rate

Full text

Pdf (1.37 MB)

Source

International Conference on Autonomous Agents archive
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2 table of contents

Budapest, Hungary

SESSION: Interactions table of contents

Pages 1255-1256

Year of Publication: 2009

ISBN:978-0-9817381-7-8

Authors

M. Kaisers	Eindhoven University of Tech, Eindhoven, The Netherlands
K. Tuyls	Eindhoven University of Tech, Eindhoven, The Netherlands
S. Parsons	Brooklyn College, Brooklyn, New York
F. Thuijsman	Maastricht University, Maastricht, The Netherlands

Sponsors

: The Foundation for Intelligent Physical Agents
Microsoft Research : Microsoft Research
: Whitestein Technologies
: European Office of Aerospace Research and Development, Air Force Office of Scientific Research, United States Air Force Research Laboratory
: Drexel University
: Wiley -- Blackwell Ltd

Publisher

International Foundation for Autonomous Agents and Multiagent Systems Richland, SC

Bibliometrics

Downloads (6 Weeks): 13, Downloads (12 Months): 22, Citation Count: 0

Additional Information:

abstract references index terms collaborative colleagues

Tools and Actions:	Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

ABSTRACT

Multi-agent learning is a challenging problem and has recently attracted increased attention by the research community [4, 5]. It promises control over complex multi-agent systems such that agents enact a global desired behavior while operating on local knowledge.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	T. Börgers and R. Sarin. Learning through reinforcement and replicator dynamics. Journal of Economic Theory, 77(1), 1997.
	2	Michael Kaisers , Karl Tuyls , Frank Thuijsman , Simon Parsons, Auction Analysis by Normal Form Game Approximation, Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, p.447-450, December 09-12, 2008 [doi>10.1109/WIIAT.2008.261]
	3	S. Parsons, M. Marcinkiewicz, J. Niu, and S. Phelps. Everything you wanted to know about double auctions, but were afraid to (bid or) ask. Technical report, Brooklyn College, City University of New York, 2900 Bedford Avenue, Brooklyn, NY 11210, USA, 2006.
	4	Yoav Shoham , Rob Powers , Trond Grenager, If multi-agent learning is the answer, what is the question?, Artificial Intelligence, v.171 n.7, p.365-377, May, 2007 [doi>10.1016/j.artint.2006.02.006]
	5	Karl Tuyls , Simon Parsons, What evolutionary game theory tells us about multiagent learning, Artificial Intelligence, v.171 n.7, p.406-416, May, 2007 [doi>10.1016/j.artint.2007.01.004]
	6	Karl Tuyls , Pieter Jan Hoen , Bram Vanschoenwinkel, An Evolutionary Dynamical Analysis of Multi-Agent Learning in Iterated Games, Autonomous Agents and Multi-Agent Systems, v.12 n.1, p.115-153, January 2006 [doi>10.1007/s10458-005-3783-9]
	7	W. E. Walsh, R. Das, G. Tesauro, and J. O. Kephart. Analyzing complex strategic interactions in multi-agent systems. In P. Gmytrasiewicz and S. Parsons, editors, Proceedings of the Workshop on Game Theoretic and Decision Theoretic Agents, pages 109--118, 2002.