Negotiation Framework Driven by Active Learning of Opponent's Preference
Jiming Li · Beijing Youdian Xueyuan xuebao · 2012
Aiming at solving automated negotiation problem,an active learning based method was proposed to learn opponent's negotiation preference.The process of negotiation was viewed as a proposal's sequence which can be mapped into bidding trajectory feature space to form sample set.Due to fierce competition,the cost of labeling samples is high.Therefore,active learning algorithm was applied to improve the prediction accuracy of opponent's negotiation preference within budget.The experimental results show that the proposed method has better prediction ability,which can reduce the number of negotiation steps and increase the overall utility of negotiation.