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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/74411

    Title: 電子化協商架構下的對手喜好預測與策略運用方法
    Other Titles: A negotiation support system considering the changing the opponent's preferences
    Authors: 黃淳韋;Huang, Chun-Wei
    Contributors: 淡江大學資訊管理學系碩士班
    張昭憲;Chang, Jau-Shien
    Keywords: 對手喜好預測;協商支援;基因演算法;自動化協商;電子商務;Opponent's preferences projection;Negotiation Support;Genetic Algorithm;Automated negotiation;e-commerce
    Date: 2011
    Issue Date: 2011-12-28 18:34:22 (UTC+8)
    Abstract: 對手喜好預測是最重要的協商支援項目之一,若能預先了解對手喜好,便有機會主導全局。綜觀目前的相關研究,我們發現仍有以下問題有待解決: 首先,當獲知對手喜好後,如何調整己方策略,以達成預設的目標。其次,協商者的喜好在協商中並非一成不變,如何快速追蹤,以免因誤判而造成損失。我們以Faratin等人提出的協商模型為基礎,配合基因演算法進行對手喜好預測,並大幅放寬了前人研究中常見的參數限制。當了解對手喜好後,我們提出多種目標式來反應預測者的長程目標與協商態度,以便在候選集中挑選合適的提案。因此,本研究提出了三種追蹤公式,以快速察覺對手喜好的變動。針對上述方法,我們使用模擬實驗進行效能驗證。實驗結果顯示,結合本研究提出的預測方法與提案策略,預測一方的效用明顯增加;此外,雙方的效用總和與公平性也同時獲得改善。上述結果說明本研究,確實能協助雙方取得較佳合約,有利於長遠合作關係的建立。
    Opponent''s preferences prediction is one of the most important negotiation supports. If opponent''s preferences are known in advance, the negotiators have better chance to obtain a win-win settlement. Even if a number of researches have been proposed for this topic, however, there are still several issues to be resolved. First, if the complete or partial knowledge of opponent’s preferences is known, the negotiator may need to adjust his strategy to achieve preset goals as efficient as possible. Secondly, if the opponent’s preferences are allowed to be changed in negotiations, the negotiator should discover such a change as soon as possible. To this end, we adopt Faratin’s negotiation model to sketch the preference of negotiators, and apply genetic algorithms to predict the opponents’ preferences by using the negotiation history as input. In addition, three objective functions are proposed to mimic various attitude changes of negotiators which affect the selection of the next proposal from the candidate sets. To trace the change of opponents’ preferences, this study proposed different weighted functions to detect the changes as soon as possible. Simulation results show that a negotiator who applies the proposed prediction method can increase his effectiveness significantly. Also, the proposed method can improve the overall utilities and fairness for both sides. The experimental results demonstrate that the proposed prediction technology really helps in achieving a better contract and is conducive to long-term cooperation relationship.
    Appears in Collections:[資訊管理學系暨研究所] 學位論文

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