身在一個不停變動的世界，作為價值鏈上的一員，不論是企業、投資者、消費者無不希望可以消弭不確定因素所衍生的種種問題。隨著經濟發展環境污染日漸嚴重，人類開始省思如何能兼顧經濟活動及環境保護，永續發展逐漸成為科技發展的重要議題。被視為永續發展的其中一種方式，產品服務系統結合了有形的產品、無形的服務及後端的支援系統，目的於降低不確定因素衍生的風險並且滿足多變的顧客需求。雖然已有許多針對產品服務系統發展之研究，但仍欠缺可因應變動環境而提供不同內容的設計方法。為使人與電腦更自然互動學者們提出普及運算，其顯著目標是使得電腦裝置可以感知周圍的環境變化，從而根據環境的變化做出基於用戶需要的行為。有鑑於普及運算之特性與設計產品服務系統之不確定性，本研究致力於發展設計個人化產品服務系統之模型以因應多變的需求，多代理人系統用於實踐普及運算概念，藉代理人間溝通協調設計出兼顧個人化差異及永續發展之產品服務系統，在此方法中使用者可以提出期望的特徵值例如預算、頻率、狀況…等，該系統會透過協同過濾演算法尋求適切的產品或服務以滿足使用者偏好，各個可能的產品服務系統會依據契合程度依序排列並提供給使用者。藉由本研究，企業可以檢視現存的產品服務系統之表現如何，除此之外更可以協助設計更符合顧客需求之個人化內容，最終提升企業之競爭力及品牌價值。

關鍵字: 產品服務系統、多代理人系統、服務工程、個人化、普及計算

In the dynamic changing world, problems of uncertainty are doubtlessly crucial issues for firms, investors, customers, and all other members in the value chain. To achieve sustainability, reduce the risk of uncertainty, and fulfill a variety of customer needs, the concept of Product Service System has been proposed as a solution. Although there are several previous studies working on PSS development, there is no dynamic methodology to quickly adjust for external changes and customer response. According to the above gap, this paper presents a Multi-Agent-based Personalized Product Service System (MAPPSS) development model. As the definition of pervasive computing, the complex problem can be solved by distributed intelligent. Multi-agent system is a well-known methodology applied in several pervasive environments. An agent-based model is built to develop a personalized PSS prototype. In this model, users are able to decide the expected service characteristics, and service composition would be conducted by searching related product and service in the database. With the application of the proposed method, competence of enterprises can be enhanced due to user-oriented features. The PSS can be improved immediately by constantly monitoring and through the iteration of the proposed method.

Keywords: Product service system, Multi-agent system, Personalization, Pervasive Computing

Abstract II
Table of Contents III
List of Figures IV
List of Tables V
1 Introduction 6
2 Literature Review 9
2.1 Product Service System 9
2.2 Pervasive Computing & Multi-Agent System 12
2.3 Personalization & Recommendation System 15
2.4 Summary 19
3 Methodology 21
3.1 Collaborative Filtering Algorithm 22
3.1.1 Item Similarity Computation 23
3.1.2 Prediction Computation 25
3.2 Multi-Agent Based Personalized Product Service System Development Model 25
3.2.1 Sensor Agent Module 28
3.2.2 Rating Agent 28
3.2.3 Similarity Agent 29
3.2.4 User Package Agent 30
3.2.5 System Agent 30
3.2.6 Product Selection Agent & Service Selection Agent 31
3.2.7 Recommendation Agent 32
3.3 System Evaluation 32
4 Case Study 35
4.1 Background 35
4.2 The application of MAPPSS 35
4.3 Performance Evaluation 45
4.4 Discussion 53
5 Conclusion 55
6 Reference 57

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