Abstract
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.
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Tran, T., Cohen, R. (2002). A Learning Algorithm for Buying and Selling Agents in Electronic Marketplaces. In: Cohen, R., Spencer, B. (eds) Advances in Artificial Intelligence. Canadian AI 2002. Lecture Notes in Computer Science(), vol 2338. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47922-8_3
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DOI: https://doi.org/10.1007/3-540-47922-8_3
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