Learning to coordinate without sharing information
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
A scalable comparison-shopping agent for the World-Wide Web
AGENTS '97 Proceedings of the first international conference on Autonomous agents
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
Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence
Multiagent Systems: A Modern Approach to Distributed 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
Economic Incentives for Information Agents
CIA '00 Proceedings of the 4th International Workshop on Cooperative Information Agents IV, The Future of Information Agents in Cyberspace
Multi-Agent Reinforcement Learning: An Approach Based on the Other Agent's Internal Model
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver
IEEE Transactions on Computers
Learning to coordinate actions in multi-agent systems
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Modeling trust using transactional, numerical units
Proceedings of the 2006 International Conference on Privacy, Security and Trust: Bridge the Gap Between PST Technologies and Business Services
A strategy for improved satisfaction of selling software agents in E-commerce
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
A dynamic reputation system with built-in attack resilience to safeguard buyers in e-market
ACM SIGSOFT Software Engineering Notes
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In this paper, we propose a reputation oriented reinforcement learning algorithm for buying and selling agents in electronic market environments. We take into account the fact that multiple selling agents may offer the same good with different qualities. 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 sellers. Selling agents learn to maximize their expected profits by adjusting product prices and by optionally altering the quality of their goods. As detailed in the paper, we believe that our proposed strategy leads to improved performance for buyers and sellers, reduced communication load, and robust systems.