Vidal's library
Title: Improving User Satisfaction in Agent-Based Electronic Marketplaces by Reputation Modelling and Adjustable Product Quality
Author: Thomas Tran and Robin Cohen
Book Tittle: Proceedings of the Third International Joint Conference on Autonomous Agents and MultiAgent Systems
Pages: 828--835
Publisher: ACM
Year: 2004
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 sellers reputation and the selling agents not adjusting product quality.

Cited by 4  -  Google Scholar

@InProceedings{tran04a,
  author =	 {Thomas Tran and Robin Cohen},
  title =	 {Improving User Satisfaction in Agent-Based
                  Electronic Marketplaces by Reputation Modelling and
                  Adjustable Product Quality},
  booktitle =	 {Proceedings of the Third International Joint
                  Conference on Autonomous Agents and MultiAgent
                  Systems},
  pages =	 {828--835},
  year =	 2004,
  publisher =	 {{ACM}},
  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 sellers reputation and
                  the selling agents not adjusting product quality.},
  keywords =     {modeling trust learning ecommerce},
  url =		 {http://jmvidal.cse.sc.edu/library/tran04a.pdf},
  comment =	 {masrg},
  googleid = 	 {v1JdKI00JqgJ:scholar.google.com/},
  cluster = 	 {12116429628359135935}
}
Last modified: Wed Mar 9 10:16:14 EST 2011