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Tri-Training based Bilateral Multi-Issue Negotiation Framework

机译：基于三培训的双边多问题谈判框架

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摘要

Negotiation has been extensively discussed in electronic commerce for decades. Recent growing interest in importing machine learning algorithm in electronic commerce has given increased importance to automated negotiation. A Tri-Training based algorithm 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, unlabeled training examples are readily available but labeled ones are fairly expensive to obtain. Therefore, Tri-Training, as a semisupervised method, was imported into negotiation framework to increase the number of samples and improve perdition accuracy of opponent’s negotiation preference learning. Based on negotiation preference of both side, an optimization algorithm is conducted to compute win-win counter proposal. The experimental results show that the proposed method can decrease the number of negotiation steps and increase the overall utility of negotiation.
机译：谈判已经在电子商务中广泛讨论了数十年。最近对于在电子商务中导入机器学习算法的兴趣日益浓厚，这对自动协商越来越重要。提出了一种基于三级训练的算法来学习对手的谈判偏好。协商过程被视为提案的顺序，可以将其映射到出价轨迹特征空间中以形成样本集。由于激烈的竞争，未加标签的训练示例很容易获得，但加标签的训练示例却相当昂贵。因此，将Tri-Training作为一种半监督方法引入了谈判框架，以增加样本数量并提高对手的谈判偏好学习的成功率。基于双方的协商偏好，进行了优化算法，计算了双赢的反提议。实验结果表明，该方法可以减少协商步骤，提高了协商的整体效用。

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