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Nash Equilibrium: Better Strategy for Agents Coordination
Dept. of Comput. Sci., Central South Univ. (CSU), Changsha, China.
Dept. of Comput. Sci., Central South Univ. (CSU), Changsha, China.
Dept. of Comput. Sci., Central South Univ. (CSU), Changsha, China.
2008 (English)In: 2008 IEEE Asia-Pacific Services Computing Conference: Proceedings, IEEE, 2008Conference paper, Published paper (Refereed)
Abstract [en]

Intelligent agents operate in an environment that can be mostly not dynamic. This requires learning by trial and error in order to know better the environment and reach their goals. They also need cooperation among themselves. We use reinforcement learning and game theory techniques to discuss how intelligent agents learn and cooperate. As known, one of the properties of agents is that they are social. They must therefore be cooperative in the social environment where they are. This is possible by learning the environment facts by doing, sharing instantaneous information and learned knowledge. The cooperative agents will perform better than independent agents. What is the advantage of such cooperation? As an example, in this paper we shall especially show how cooperative trading agents can maximize their profits due to a good coordination by playing Nash equilibrium to ensure that each agent chooses the best strategy which gives a good payoff.

Place, publisher, year, edition, pages
IEEE, 2008.
Keywords [en]
Nash equilibrium, Multiagent, Reinforcement learning, Game theory
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:oru:diva-59155DOI: 10.1109/APSCC.2008.179ISBN: 978-0-7695-3473-2 (print)OAI: oai:DiVA.org:oru-59155DiVA, id: diva2:1134995
Conference
The 3rd IEEE Asia-Pacific Services Computing Conference (APSCC 2008), Yilan, Taiwan, December 9-12, 2008
Available from: 2017-08-22 Created: 2017-08-22 Last updated: 2017-10-18Bibliographically approved

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Mutimukwe, Chantal

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CiteExportLink to record
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Citation style
  • apa
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  • en-US
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • asciidoc
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