The Michigan Internet AuctionBot: a configurable auction server for human and software agents
AGENTS '98 Proceedings of the second international conference on Autonomous agents
REGRET: reputation in gregarious societies
Proceedings of the fifth international conference on Autonomous agents
Using Inter-agent Trust Relationships for Efficient Coalition Formation
AI '02 Proceedings of the 15th Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
A Social Mechanism of Reputation Management in Electronic Communities
CIA '00 Proceedings of the 4th International Workshop on Cooperative Information Agents IV, The Future of Information Agents in Cyberspace
A trust model for distributed systems based on reputation
International Journal of Web and Grid Services
A comprehensive and adaptive trust model for large-scale P2P networks
Journal of Computer Science and Technology - Special section on trust and reputation management in future computing systmes and applications
PATROL: a comprehensive reputation-based trust model
International Journal of Internet Technology and Secured Transactions
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
Hi-index | 0.00 |
We propose a reputation oriented reinforcement learning algorithm for buying agents in electronic market environments. We take into account the fact the quality of a good offered by different selling agents may not be the same, and a selling agent may alter the quality of its goods. In our approach, buying agents learn to avoid the risk of purchasing low quality goods and to maximize their expected value of goods by dynamically maintaining sets of reputable and disreputable sellers. Modelling the reputation of sellers allows buying agents to focus on those sellers with whom a certain degree of trust has been established. We also include the ability for buying agents to explore the marketplace in order to discover new reputable sellers. In this paper, we focus on presenting the experimental results that confirm the improved satisfaction for buying agents that model reputation accordingto our algorithm.