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研究生:余丁榮
研究生(外文):Ting-Jung Yu
論文名稱:模糊限制代理人協商之對手行為模式學習
論文名稱(外文):Beliefs Learning in Fuzzy Constraint-directed Agent Negotiation
指導教授:賴國華
指導教授(外文):K. Robert Lai
學位類別:博士
校院名稱:元智大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:英文
論文頁數:105
中文關鍵詞:智慧型系統多重代理人系統代理人協商對手模塑模糊限制
外文關鍵詞:Intelligence systemsMulti-agent systemsAgent negotiationOpponent modelingFuzzy constraints
相關次數:
  • 被引用被引用:1
  • 點閱點閱:125
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  • 下載下載:0
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本論文之主要目的是期望在資訊不完整的代理人協商環境中,藉由學習對手的行為模式,來更為貼近現實世界中的解題情境,提昇協商結果的品質及代理人的整體效能。

我們首先以模糊限制為基礎,建構完整的代理人協商架構,包括基本的協商定義、協商流程、訊息交換協定及協商計算模式。然後,在此協商架構下,探討學習對手行為模式的相關議題,涵括對手行為模式的學習架構及其計算模式。藉由對手的交談訊息中,代理人可以歸納出對手的(1)喜好函數、(2)議題重要性、及(3)協商策略等特性來掌握對手的行為狀態。 另外,我們也利用協商案例庫,比對出近似案例,進而預測對手可能的讓步行為,加速達成協商的預期目標。

針對學習計算模式,我們提出兩種學習演算法:貝式及模糊機率,以因應不同的協商情境。實驗數據,包括收斂速度及協商結果,顯示貝氏學習比較適用在長期穩定的協商環境,而模糊機率學習則比較有利於變動頻繁的協商環境,推理出接近對手的行為模式。最後,並以保險業之雙方協商及產業供應鏈之多方協商等協商應用,來驗證研究的實用性。
This dissertation presents a framework for learning opponent’s beliefs to improve the quality of negotiation and the overall competence of each agent in a multi-agent problem-solving environment.

We first present a fuzzy constraint-directed approach to agent negotiation, including basic definitions, behavior model, communication messages, and computational model of negotiation. Then, this negotiation framework is extended to incorporate a learning element for capturing opponent’s beliefs. Through exchange messages, agent can deduce the features of opponent’s (1) preference functions, (2) the importance of issues, and (3) negotiation strategy and to predict opponent’s behavior state. Additionally, we also construct an instance repository for matching the proximate instances to predict opponent’s next feasible proposals and to speed up the convergence of the expected negotiation goal.

For beliefs learning, two learning models are proposed, Bayesian learning and fuzzy probability learning, for different negotiation environments, The results of numerical experiments including the speed of convergence and the quality of negotiation reveal that the Bayesian learning method fairs better at regularizing the opponent’s behavior models in a long-term stable environment, while the fuzzy probability learning approach is more adept for approximating the opponent’s behavior patterns in short-term volatile situation.

Finally, to demonstrate the practicality of the proposed approach, two real-world example applications, a bi-lateral multi-issue negotiation for insurance policy and a multi-lateral negotiation for supply chain, are successfully implemented and also exhibit the benefits of learning-enable agent negotiation.
Contents

List of Figures xii
List of Tables xiv
Symbol Definitions xvi
1. Introduction 1
1.1 Agent Negotiation 1
1.2 Learning Opponent''s Beliefs to Support Agent Negotiation 3
1.3 Scope of the Work 5
1.4 Dissertation Organization 7
2. Agent Negotiation as Distributed Fuzzy Constraint Satisfaction
Problem 9
2.1 Basic Definitions 9
2.2 Process of Agent Negotiation 13
2.2.1 Behavior Model 13
2.2.2 Communication Messages 14
2.3 Offer Generation 17
2.4 Summary 24
3. Agent Negotiation with Learning Element 26
3.1 Design of Learning Element 26
3.1.1 Proposed Framework 27
3.1.2 Computational Model 31
3.1.3 Algorithm 34
3.2 Bayesian Beliefs Learning 37
3.3 Fuzzy Probability Beliefs Learning 41
3.4 Summary 45
4. Numerical Experiments 48
4.1 Correctness of Strategy Identification 48
4.1.1 Convergence Process for a Single Instance 49
4.1.2 Average Convergence Speed for 500 Instances 54
4.2 Quality of Solution 60
4.2.1 Stable Environments 61
4.2.2 Volatile Environments 66
4.2.3 Semi-stable and Volatile Environments 71
4.3 Summary 75
5. Applications 78
5.1 Bi-lateral Multi-issue Negotiation 78
5.1.1 Life Insurance Policy 78
5.1.2 Negotiation Outcomes 82
5.2 Multi-lateral Multi-issue Negotiation 85
5.2.1 Supply Chain 85
5.2.2 Negotiation Outcomes 87
5.3 Summary 91
6. Conclusions 93
6.1 Contributions 93
6.2 Limitations and Future Directions 95
Bibliography 97
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