The purpose of study is to develop intelligent negotiation agents that can behave rationally so as to improve the final outcomes in a one-to-many negotiation. A Bayesian learning model of multi-attribute one-to-many negotiation, namely Bayes Improved-ITA is proposed. These agents employ Bayesian belief updating process to model their opponent's utility structure. The performance of Bayes Improved-ITA is promising when it is compared with the results of one-to-many negotiations that use genetic-based machine learning model and heuristic search algorithm. Results from the experimental work show that having knowledge of opponent's preferences and constraints, negotiation agents can achieve more optimal outcomes.
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