关键词: 双边协商, 贝叶斯后验模型, 马尔可夫链, 蒙特卡罗方法

Abstract: To improve the effectiveness of automated bilateral multi-issue negotiation, a Bayesian posterior model was set up. It was trained through historical data of negotiations and learned negotiation preference of opponents. Moreover, negotiation counter proposal was suggested according to opponents preference so as to improve the efficiency of negotiations. Hypothesis space was complex multi-dimensions continuous function, Markov Chain Monte Carlo(MCMC) method was used to sample the space. Therefore the computing speed of Maximum a Posterior (MAP) was improved in Bayesian model. Experimental data showed that the proposed model could improve efficiency of negotiation, reduce negotiation rounds and improve the whole negotiation utility.

Key words: bilateral negotiation, bayesian posterior model, Markov Chain, Monte Carlo methods