本論文以電子化協商為基礎，發展兼具效率、準確性與實用性的對手喜好預測方法。首先，本研究結合Raiffa提出的加總計分模型與Faratin等人提出之策略模型，做為將對手協商行為量化的依據。根據此綜合模型，便可明確定義與對手喜好預測直接相關之待解問題。接下來，我們以基因演算法為基礎，發展預測演算法，以便同時顧及效率與準確性。由於求解的速度與準確性可藉由演化的代數來控制，使用者可在協商中依據臨場需求來設定解的品質。此外，前人研究中對於議題數、協商戰略係數與效用函數等參數的諸多限制，在此也被大幅放寬，將可有效提升預測結果之實用性。實驗結果顯示:本研究提出之方法能準確預測對手喜好，使用預測的一方也可獲得顯著的利益。根據統計，當雙方各議題出價區間相同時，使用預測的一方平均可取得15.3%的效用改善；當議題出價區間僅有80%重疊狀況下，也可增加14.3%的效用。值得注意的是:雖然使用預測的一方效用增加，但對手的收益並未因此而減損，顯示預測的一方有能力主導雙贏。

The thesis intends to develop effective methods to accurately predicts the opponent’s preferences for negotiation in e-commerce. First, the negotiation behavior of opponent is quantified by combining the scoring additive model and Faratin’s strategy model. According to the combined model, the problems of predicting the opponent’s preferences can be defined concisely. Then, we develop a prediction method based on genetic algorithm to compromise effectiveness and accuracy. Because the quality of solutions can be controlled by the number of evolution generations, users can easily adjust their goal according to the time limit. Moreover, the restriction on the number of issues, the coefficients of negotiation tactics and utility functions in previous works can be greatly relaxed by the proposed method. The experimental results reveal that our method can predict the opponent’s preference correctly. Besides, the results also show that adopting the prediction method can bring considerable profit. According to the statistics of experiment results, the predicting side can increase 15.3% utility when the offer zone of each issue is fully-overlapped; and the utility increase 14.3% when the offer zone of each issue is 80%-overlapped. In particular, although the utility of the predicting side is increased, the opponent’s profit doesn’t decrease for this reason. This result demonstrate that the predicting side can dominant the negotiation, meanwhile, maintain a win-win settlement.

目  錄
第1章 前言	1
第2章 協商相關理論簡介	6
第1節 協商者之喜好表示	6
第2節 協商結果之評估指標	7
第3節 基因演算法簡介	9
第3章 對手喜好預測	13
第1節 問題陳述	13
第2節 使用基因演算法進行預測	14
第3節 預測演算法	17
第4節 讓步策略	20
第4章 實驗結果	22
第1節 實驗設計	22
第2節 協商結果之評估指標分析	23
第3節 預測準確度分析	28
第4節 喜好預測對於協商動線之影響	29
第5章 結論	31
參考文獻	33
附錄A	常用的效用函數	36
附錄B	協商戰略與策略	37
附錄C	多議題協商實驗數據	40

 
圖目錄
圖 2 1  雙方的共同可行解區域	8
圖 2 2  公平的成交點	9
圖 2 3  基因演算法之演化流程圖	10
圖 2 4  交配過程示意圖	11
圖 2 5  突變過程示意圖	12
圖 3 1  預測的對手喜好之基因編碼	16
圖 3 2  協商預測流程	18
圖 3 3  讓步策略	21
圖 4 1  多議題協商之預測方效用	27
圖 4 2  多議題協商之非預測方效用	27
圖 4 3  多議題協商之與效率前緣中點距離	28
圖 4 4  預測效用誤差圖	29
圖 4 5  NP-NP協商動線	30
圖 4 6  NP-P協商動線	30
圖 B 1  時間變量圖	38


 
表目錄
表 3 1  預測的對手喜好	16
表 4 1  出價範圍100%重疊之喜好設定	23
表 4 2  出價範圍80%重疊之喜好設定	23
表 4 3  協商雙方出價範圍100%重疊實驗結果	24
表 4 4  協商雙方出價範圍80%重疊實驗結果	24
表 4 5  整合型協商之喜好設定	25
表 4 6  分配型協商之喜好設定	25
表 4 7  整合型協商實驗結果	26
表 4 8  分配型協商實驗結果	26
表 4 9  協商者1喜好設定	29
表 4 10  協商者2喜好設定	29
表 A 1  單一議題之效用函數	36
表 C 1  3-ISSUES實驗結果	40
表 C 2  4-ISSUES實驗結果	40
表 C 3  5-ISSUES實驗結果	40
表 C 4  6-ISSUES實驗結果	40

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