Learning optimal seller strategies with intelligent agents: Application of evolutionary and reinforcement learning

Riyaz T. Sikora, Vishal Sachdev

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The role of automated agents in the electronic marketplace has been growing steadily and has been attracting a lot of research from the artificial intelligence community as well as from economists. We consider the problem of homogeneous sellers of a single raw material or component vying for business from a single large buyer, and present artificial agents that learn near-optimal seller strategies. Standard game-theoretic analysis of the problem assumes completely rational and omniscient agents to derive Nash equilibrium seller policy. We show that in our problem such an equilibrium is unstable, and present simple reinforcement and evolutionary learning agents that learn strategies with better than Nash payoffs.

Original languageEnglish (US)
Title of host publication15th Workshop on Information Technology and Systems, WITS 2005
PublisherUniversity of Arizona
Pages75-80
Number of pages6
StatePublished - 2005
Externally publishedYes
Event15th Workshop on Information Technology and Systems, WITS 2005 - Las Vegas, NV, United States
Duration: Dec 10 2005Dec 11 2005

Other

Other15th Workshop on Information Technology and Systems, WITS 2005
CountryUnited States
CityLas Vegas, NV
Period12/10/0512/11/05

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems

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    Sikora, R. T., & Sachdev, V. (2005). Learning optimal seller strategies with intelligent agents: Application of evolutionary and reinforcement learning. In 15th Workshop on Information Technology and Systems, WITS 2005 (pp. 75-80). University of Arizona.