کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
380036 659529 2008 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Using temporal-difference learning for multi-agent bargaining
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Using temporal-difference learning for multi-agent bargaining
چکیده انگلیسی

This research treats a bargaining process as a Markov decision process, in which a bargaining agent’s goal is to learn the optimal policy that maximizes the total rewards it receives over the process. Reinforcement learning is an effective method for agents to learn how to determine actions for any time steps in a Markov decision process. Temporal-difference (TD) learning is a fundamental method for solving the reinforcement learning problem, and it can tackle the temporal credit assignment problem. This research designs agents that apply TD-based reinforcement learning to deal with online bilateral bargaining with incomplete information. This research further evaluates the agents’ bargaining performance in terms of the average payoff and settlement rate. The results show that agents using TD-based reinforcement learning are able to achieve good bargaining performance. This learning approach is sufficiently robust and convenient, hence it is suitable for online automated bargaining in electronic commerce.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Electronic Commerce Research and Applications - Volume 7, Issue 4, Winter 2008, Pages 432–442
نویسندگان
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