A Reputation and Learning Model for Electronic Commerce Agents

In this paper, reinforcement learning is used in order to model the reputation of buying and selling agents. Two important factors, quality and price, are considered in the proposed model. Each selling agent learns to evaluate the reputation of buying agents, based on their profits for that seller and uses this reputation to dedicate a discount for reputable buying agents. Also, selling agents learn to maximize their expected profits by using reinforcement learning to adjust the quality and price of the products, in order to satisfy the buying agents' preferences. In contrast, buying agents evaluate the reputation of selling agents based on two different factors: Reputation based on quality and price. Therefore, buying agents avoid interacting with disreputable selling agents. In addition, the fact that buying agents can have different priorities on the quality and price of their goods is taken into account. The proposed model has been implemented with Aglet and tested in a large-sized marketplace. The results show that selling/buying agents that use the proposed algorithms in this paper obtain more satisfaction than the other selling/buying agents.