Abstract—In this paper we propose a model based on personality for buyer and seller agents in agent-based electronic marketplaces. The personality of buyer agents affects their behavior in market. Buyer agents use their own personality to evaluate the value of seller agents’ bids. Also buyer agents apply reinforcement learning to evaluate the reputation of seller agents and then focus their trading on reputable sellers. On the other hand, the personality of seller agents affects them to consider discount for buyer agents. In addition, seller agents apply reinforcement learning to model the reputation of buyer agents. We have implemented this model with aglet which is a java based environment for building agents. Our results show that sellers with low score of stingy earn more benefits in comparison with high stingy sellers. Also, conscientious seller agents gain more reputation relative to conscienceless seller agents. On the other hand, buyer agents with high score of openness and low score of stingy purchase more new goods and more expensive goods relative to buyers with low score of openness and high score of stingy.
Index Terms—Reputation, Reinforcement Learning, Electronic Commerce Agents, personality.
Aboozar Barzegar is with the Department of Computer Engineering, Islamic Azad University, Lamerd, Iran. (Phone: +98-09171846503; email: abooar.barzegar@yahoo.com).
Adel Jahanbani is with the Department of Computer Engineering, Islamic Azad University, Lamerd, Iran. (Phone: +98-09173819458).
Omid Roozmand is with the Department of Computer Engineering, Isfahan University, Isfahan, Iran. (Phone: +98-9360362758)
Cite: Aboozar Barzegar, Adel Jahanbani and Omid Roozmand, "Openness, Conscientiousness and Stingy for Buyer and Seller Agents in Electronic Marketplace," International Journal of Computer Theory and Engineering vol. 2, no. 3, pp. 358-363, 2010.
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