Improving User Satisfaction in Agent-Based Electronic Marketplaces by Reputation Modelling and Adjustable Product Quality

  • Authors:

  • Thomas Tran;Robin Cohen

  • Affiliations:

  • University of Waterloo;University of Waterloo

  • Venue:
  • AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
  • Year:
  • 2004

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Abstract

In this paper, we propose a market model and learning algorithms for buying and selling agents in electronic marketplaces. We take into account the fact that multiple selling agents may offer the same good with different qualities, and that selling agents may alter the quality of their goods. We also consider the possible existence of dishonest selling agents in the market. In our approach, buying agents learn to maximize their expected value of goods using reinforcement learning. In addition, they model and exploit the reputation of selling agents to avoid interaction with the disreputable ones, and therefore to reduce the risk of purchasing low value goods. Our selling agents learn to maximize their expected profits by using reinforcement learning to adjust product prices, and also by altering product quality to provide more customized value to their goods. This paper focuses on presenting results from experiments investigating the behaviours of buying and selling agents in large-sized electronic marketplaces. Our results confirm that buying and selling agents following the proposed algorithms obtain greater satisfaction than buying and selling agents who only use reinforcement learning, with the buying agents not modelling sellersý reputation and the selling agents not adjusting product quality.