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研究生:楊淯程
研究生(外文):Yu-chen Yang
論文名稱:使用輻射基底函數網路建構多屬性效用函數
論文名稱(外文):Using Radial Basis Function Networks to Model Multi-attribute Utility Functions
指導教授:林福仁林福仁引用關係
指導教授(外文):Fu-ren Lin
學位類別:碩士
校院名稱:國立中山大學
系所名稱:資訊管理學系研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:英文
論文頁數:67
中文關鍵詞:決策分析效用函數輻射基底函數多屬性效用理論簡易多屬性評等技術
外文關鍵詞:MAUTSMARTSDecision AnalysisSMARTERUtility FunctionRBF
相關次數:
  • 被引用被引用:4
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  • 下載下載:43
  • 收藏至我的研究室書目清單書目收藏:0
隨著電子商務的蓬勃發展,許多線上協商、議價系統必須依賴決策分析領域中的效用函數協助使用者找出可行解決方案,或者是對使用者效用最大的方案。因此,如何快速、正確地建構使用者效用函數,成為一個重要的課題。本研究提出人工智慧中的輻射基底函數類神經網路方法,期能更為快速、精確地建構使用者的效用函數。透過實驗驗證此方法的可行性,筆者分別將輻射基底函數、複回歸、簡易多屬性評等技術等建構效用函數的方法,就「預測效能」、「花費時間」以及「使用者對這些方法的認知」作比較。實驗結果證實本研究所提出的輻射基底網路方法可行,不僅比過去決策分析所常用的簡易多屬性評等技術省時,同時也較複回歸、簡易多屬性評等技術等方法精確。
On-line negotiation and bargaining systems can work effectively on the Internet based on the prerequisite that user utility functions are known while undergoing transactions. However, this prerequisite is hard to meet due to the variety and anonymous nature of Internet surfing. Therefore, how to rapidly and precisely construct a user’s utility function is an essential issue. This research proposes a radial basis function (RBF) network, a neural network, to model a user’s utility function in order to rapidly and precisely model user utility function. We verify the feasibility of the method through experiments, and compare the performance of RBF networks in prediction performance, time expenses, and subjects’ perceptions with the Multiple Regression (MR), SMARTS, and SMARTER methods. The results show that the RBF network method is feasible in these criteria. Not only the RBF network needs less time to construct the users’ utility function than the SMARTS method does, but also it can model user utility functions more precisely than the MR, SMARTS, and SMARTER methods.
中文摘要 III
ABSTRACT IV
TABLE OF CONTENTS V
LIST OF TABLES VII
LIST OF FIGURES VIII
CHAPTER 1 INTRODUCTION 1
1.1 RESEARCH BACKGROUND 1
1.2 RESEARCH MOTIVATION 1
1.3 RESEARCH OBJECTIVES 2
CHAPTER 2 LITERATURE REVIEW 3
2.1 MULTI-ATTRIBUTE UTILITY THEORY 3
2.1.1 Attributes 3
2.1.2 Single-dimension Utility Functions 4
2.1.3 Model Types 4
2.1.4 Weights 5
2.2 HOW TO MODEL A MULTI-ATTRIBUTE UTILITY 6
2.3 RADIAL BASIS FUNCTION NETWORK 9
2.3.1 Framework of Radial Basis Function Network 9
2.3.2 Training Algorithm 11
CHAPTER 3 USING RBF NETWORKS TO MODEL MAU FUNCTIONS 15
3.1 WHY TO USE RBF NETWORK TO MODEL MAU FUNCTIONS 15
3.2 RBF NETWORK LEARNING PROCEDURE TO MODEL MAU FUNCTIONS 16
CHAPTER 4 EXPERIMENTAL DESIGN 21
4.1 DECISION PROBLEMS 21
4.2 SUBJECTS 22
4.3 EXPERIMENTAL PROCEDURE 24
4.4 MODEL CONSTRUCTION 26
CHAPTER 5 EXPERIMENTAL RESULTS 28
5.1 PREDICTION PERFORMANCE 28
5.1.1 How well to predict a subject’s ordinal preferences 28
5.1.2 How well to predict interval preferences 31
5.1.3 Prediction Errors 33
5.1.4 Whether the construction treatments change subjects preferences 35
5.3 SUBJECT PERCEPTIONS TO RBF AND SMARTS 40
5.4 DISCUSSIONS 41
CHAPTER 6 CONCLUSION AND FURTHER WORK 44
6.1 CONCLUSIONS 44
6.2 FURTHER WORK 45
APPENDIX A 49
THE INSTRUMENTS USED FOR JOB SEARCH DECISION PROBLEMS IN EXPERIMENTS 49
A.1 The description of job search decision problem 49
A.2 Utility Function Construction using SMARTS 50
A.3 Utility Function Construction for RBF Network 54
A.4 Pre-test Benchmark Questions 55
A.5 Post-test Benchmark Questions 56
APPENDIX B 57
THE INSTRUMENTS USED FOR APARTMENT RENTING DECISION PROBLEMS IN EXPERIMENTS 57
B.1 The description of job search decision problem 57
B.2 Utility Function Construction using SMARTS 58
B.3 Utility Function Construction for RBF Network 64
B.4 Pre-test Benchmark Questions 65
B.5 Post-test Benchmark Questions 66
APPENDIX C 67
C.1 QUESTIONNAIRE USED FOR ASKING SUBJECTS’ PERSPECTIVES TOWARD MAU FUNCTION CONSTRUCTION PROCESS 67
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