議價是購物流程當中十分常見的行為，而現在，網路議價也成為電子交易當中的一股潮流。為了要增進線上自動議價的效能，我們在這篇論文裡發展了三個使用在多重代理人系統的演算法。第一個演算法就是議價型樣歸納演算法，用來從原始的交易資料中歸納出一些共通的議價型樣。第二個演算法是型樣配對演算法，功用是在線上即時從型樣資料庫中辨認出適用於現行交易的型樣，並套用之。我們也另外設計了一個動態出價演算法來處理現有型樣通通不適用的狀況，這個演算法使用了效用函數以及風險態度的概念來決定三件事：當下賣方應出什麼價、應當在何時成交，以及是不是應當出一個新的價錢。我們設計了一個實驗來評估這些演算法在不同的風險態度之下的表現，並和當下較為常見的自動議價系統做一個簡單的比較。實驗結果顯示本研究所發展的系統有明顯的效能提升。本研究主要的貢獻在於將資料採礦技術的概念應用以提升電子商務中自動議價處理的能力。

Bargaining is a frequent activity in the shopping process, and it becomes a trend in electronic trading. In order to facilitate the on-line automatic bargaining activity, we develop three algorithms on the multi-agent system in this thesis. The first algorithm is the pattern generalization algorithm used for generalizing common patterns from transaction records. The second one is the pattern matching algorithm used on-line for identifying possible bargaining patterns from the pattern bases. To deal with the situation that there is no matched pattern, we design the dynamic price issuing algorithm using the utility theory to determine the seller’s price and the timing a deal should be closed. We conducted a series of field experiments to evaluate the proposed algorithms on different seller’s risk perspectives and compared the performance with conventional bargaining methods. The results show that the proposed methods obtain encouraging performance. The major contribution of this research is the initiation efforts on developing data mining algorithms for facilitating the price bargaining process for e-commerce.

Abstract II
中文摘要 III
List of Figures VI
List of Tables VII

Chapter 1. Introduction 1
1.1 Background 1
1.2 Motivations 3
1.3 Research Objectives 4
1.4 Research Results and Contributions 4
1.5 Thesis Framework 5
Chapter 2. Literature Review 7
2.1 Intelligent Agents and Multi-agent Systems 7
2.2 Bargaining in Negotiation 9
2.3 Time Series Analysis in Data Mining 11
2.4 Utility Function and Risk Attitudes 14
2.5 Automatic Bargaining Experimental System in Electronic Commerce 16
Chapter 3. The On-line Dynamic Bargaining Agents 22
3.1 The Framework of On-line Dynamic Bargaining System 22
3.2 Bargaining Pattern Generalization 23
3.3 Bargaining Pattern Matching 28
3.4 Dynamic Price Issuing 30
Chapter 4. System Implementation and Experimental Design 36
4.1 System Implementation 36
4.2 Experimental Design 37
4.2.1 System Settings for Experimental Bargaining Systems 38
4.2.2 Evaluation of the Bargaining Systems 40
4.2.3 Hypothesis Generation 42
Chapter 5. Experimental Results and Discussions 44
5.1 Subject Profile 44
5.2 Evaluation of the OLDyB 45
5.2.1 The Evolution of Hit Rate 45
5.2.2 The Trend of Financial Gain 47
5.3 Hypothesis Testing 51
Chapter 6. Conclusions and Future Research 57
6.1 Conclusions 57
6.2 Limitations 58
6.3 Future Research 58
References 60

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