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系統識別號 U0002-2406201110311800
中文論文名稱 電子化協商架構下的對手喜好預測與策略運用方法
英文論文名稱 A Negotiation Support System considering the changing the opponent's preferences
校院名稱 淡江大學
系所名稱(中) 資訊管理學系碩士班
系所名稱(英) Department of Information Management
學年度 99
學期 2
出版年 100
研究生中文姓名 黃淳韋
研究生英文姓名 Chun-Wei Huang
學號 698630125
學位類別 碩士
語文別 中文
口試日期 2011-05-28
論文頁數 50頁
口試委員 指導教授-張昭憲
委員-戚玉樑
委員-高有成
委員-張應華
中文關鍵字 對手喜好預測  協商支援  基因演算法  自動化協商  電子商務 
英文關鍵字 Opponent's preferences projection  Negotiation support  Genetic algorithm  Automated negotiation  E-commerce 
學科別分類 學科別社會科學管理學
學科別社會科學資訊科學
中文摘要 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。
英文摘要 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.
論文目次 目錄
目錄 III
表目錄 V
圖目錄 VI
第一章 前言 1
第二章 相關理論與技術 6
2.1 效用模型 6
2.2 自動化協商策略 8
2.3 提案表與目標提案的選擇 11
2.4 基因演算法 12
第三章 考量對手喜好變動之協商支援方法 15
3.1 對手喜好預測方法 15
3.2 在候選提案集中的選擇策略 17
I. 以長程目標為考量之選取方式 18
II. 以提案效果為基礎之選取方式 19
3.3 快速追蹤對手的喜好變動 22
第四章 系統實作 25
4.1 對手喜好預測之問題複雜度 25
4.2 基因編碼 26
4.3 系統運作流程 30
第五章 實驗結果與討論 32
5.1 實驗設計 32
5.2 實驗結果之評量指標 33
5.3 各種協商模式下之實驗結果比較 34
5.3.1 整合式與分配式協商的預測實驗結果 34
5.3.2 不同預測目標式的差異 36
5.3.3 應用預測方法於不同讓步計算方式 37
5.4 對手喜好變動下之預測結果 39
第六章 結論與未來展望 42
參考文獻 43
附錄 A:各種喜好變動追蹤模式之預測結果比較表 47
附錄 B:JGAP簡介 50

表目錄
表2-1 常見的議題效用函數 8
表2-2 提案表範例 11
表3-1 雙方Etype值組合所代表的協商意義 21
表4-1 基因編碼之喜好參數表列 26
表4-2 效用模型中的β值 28
表4-3 時間戰略中的β值 28
表4-4 議題共用參數 29
表5-1 應用預測方法於整合式(Integrative)與分配式(Distributive)協商 36
表5-2 選擇提案時使用方式差異 37
表5-3 二種不同的候選提案產生方式之比較 38
表5-4 權重模式對於喜好變動的追蹤效果(整合式→分配式) 40
表5-5 權重模式對於喜好改變時的協商影響(分配式→整合式) 41

圖目錄
圖2-1 基因演算法中的交配 13
圖2-2 基因演算法中的突變 14
圖3-1 協商動線範例 16
圖4-1 利用β值控制效用函數的曲度 27
圖4-2 基因編碼圖示 29
圖4-3 基因編碼時所需的位元數 30
圖4-4 系統流程圖 31
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