The proposed model labels the negotiation history data automatically by making full use of the implicit information in negotiation history．Then，the labeled data become the training samples of least-squares support vector machine that outputs the estimation of opponent’s utility function．After that，the self s utility function and the estimation of opponent’s utility function constitute a constraint optimization problem that will be further figured out by genetic algorithm．The optimal solution is the counter-ofer of onesel~ Experimental results show that the proposed model is efective and efi cient in environments where information is private and the prior knowledge is not available．

AutomatedNegotiatioDecisionModelasedonMachineLearning.PDF

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