教學大綱表 (113學年度 第1學期)
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課程名稱
Course Title		 (中文) 知識工程
(英文) Knowledge Engineering 		開課單位
Departments		 資訊工程研究所
課程代碼
Course No.		 I5850
授課教師
Instructor		 林宜隆
學分數
Credit		 3.0 必/選修
core required/optional		 選修 開課年級
Level		 研究所
先修科目或先備能力(Course Pre-requisites)：會使用任何一種電腦程式語言 or Data Base System
課程概述與目標(Course Overview and Goals)：課程概述與目標：Knowledge engineering(KE) deals with knowledge acquisition, representation, validation, inferencing, explanation, and maintenance. Knowledge engineering involves the cooperation of human experts in codifying and making the rules (or other procedures) that a human expert uses to solve real problems explicit.
1. Know the capabilities of current technology in knowledge-based system(KBS) and Knowledge Engineering (KE).
2. Be able to recognize what problems are appropriate for Knowledge Engineering (KE/KBS).
3. Select an appropriate knowledge representation(KR) and reasoning method(IE), and anticipate potential difficulties in developing and introducing the Knowledge Engineering (KE/KBS).
@Knowledge engineering studies the representation, acquisition, reasoning, decision-making, and application of knowledge [8], including big data, machine learning, data mining and knowledge discovery, uncertain reasoning, knowledge mapping, machine theorem proving, expert system, machine game, digital library, etc.
教科書(Textbook) 1.Knowledge Engineering and Management(KEM)-The CommonKADS Methodology(2000) free Book(P474) https://www.researchgate.net/publication/235705607_Knowledge_Engineering_and_Management_-_The_CommonKADS_Methodology
參考教材(Reference) 1. An Introduction to Knowledge Engineering 2007th 版本
作者 Simon Kendal (Author), Malcolm Creen (Contributor)
2.Principles of Expert Systems by Peter J.F. Lucas & Linda C. van der Gaag 1991
3. 高級專家系統：原理、設計及應用（第二版）2014 (博客來/出版地：中國) https://www.books.com.tw/products/CN11151682
4.Knowledge engineering: Principles and methods
https://www.sciencedirect.com/science/article/abs/pii/S0169023X97000566
課程大綱 Syllabus 學生學習目標
Learning Objectives		 單元學習活動
Learning Activities		 學習成效評量
Evaluation		 備註
Notes
序
No.		 單元主題
Unit topic		 內容綱要
Content summary
1 INTRODUCTION TO Knowledge engineering(KE) 1. What Is the Knowledge engineering(KE)?
2. Advantages of Knowledge engineering(KE)
3. Knowledge engineering(KE) Applications and Domains 		1. understand what is the Knowledge engineering(KE)
2. understand the advantages of the Knowledge engineering(KE).
3. understand the applications of the Knowledge engineering(KE).		 講授
 		 
2 KNOWLEDGE REPRESENTATION (I) 1. The Meaning of Knowledge
2. Production rules
3. Semantic Nets
4. Object-Attribute-Value Triples
5. Frames
6. Clips Expert Systems 		1. Understand the different types of knowledge representation
2. Practice production rules in Clips expert system		 講授
 		問答
 		 
3 KNOWLEDGE REPRESENTATION (II) 1. The Meaning of Knowledge
2. Production rules
3. Semantic Nets
4. Object-Attribute-Value Triples
5. Frames
6. Clips Expert Systems 		1. Understand the different types of knowledge representation
2. Practice production rules in Clips expert system		 講授
 		問答
 		 
4 INTRODUCTION TO the KE Tools 1. Facts, Adding and Removing Facts.
2. The Components of a Rule
3. Using Multiple Rules 		1. understand how to represent facts in the KE tools.
2. understand how to represent rules in the KE Tools.
3. understand how to start, run and exit the KE Tools.		 講授
 		報告
 		 
5 INFERENCE ENGINE (I) --knowledge engineering methodologies 1. State and Problem Spaces
2. Rules of Inference
3. Forward and Backward Chaining 		1. understand the components of inference engines.
2. understand how inference engines work.
3. understand how the inference engine of Clips expert system		 講授
 		報告
 		 
6 INFERENCE ENGINE (II) --knowledge engineering methodologies 1. State and Problem Spaces
2. Rules of Inference
3. Forward and Backward Chaining 		1. understand the components of inference engines.
2. understand how inference engines work.
3. understand how the inference engine of Clips expert system		 講授
實作
 		報告
 		 
7 Rule-Based Expert Systems(knowledge engineering methodologies in AI) 1. Variables in Rule-Based Expert Systems.
2. Fact Addresses
3. Single-Field Wildcards
4. Multifield Wildcards and Variables
5. Field Constraints 		1. understand the constructs using inRule-Based Expert Systems.
2. understand howRule-Based Expert Systems in the KE Tools.		 講授
 		問答
 		 
8 Frame-Based Expert Systems(knowledge engineering methodologies in AI) 1. Variables in Frame-Based Expert Systems.
2. Fact Addresses
3. Single-Field Wildcards
4. Multifield Wildcards and Variables
5. Field Constraints 		1. understand the constructs using in Frame-Based Expert Systems.
2. understand how Frame-Based Expert Systems in the KE Tools.		 講授
 		問答
 		 
9 期中考 期中考 期中考 期中考
 		 
10 Reasoning Under Uncertainty (I)( knowledge engineering methodologies in AI) 1. Uncertainty
2. Compound Probabilities
3. Conditional Probabilities
4. Uncertainty in Inference Chains
5. The Combination of Evidence 		1. understand how to represent uncertainty in the KE Tools.
2. understand how to use uncertainty in Clips.		 講授
實作
閱讀討論
 		 
11 Reasoning Under Uncertainty (II)( knowledge engineering methodologies in AI) 1. Uncertainty
2. Compound Probabilities
3. Conditional Probabilities
4. Uncertainty in Inference Chains
5. The Combination of Evidence 		1. understand how to represent uncertainty in expert systems.
2. understand how to use uncertainty in Clips.		 講授
實作
 		報告
 		 
12 Fuzzy expert systems (knowledge engineering methodologies in AI) 1. Salience
2. Phases and Control Facts
3. Modules and Execution Control
4. Fuzzy expert systems 		1. understand how to prioritize rules.
2. understand how to group knowledge using module
3. understand Fuzzy expert systems		 講授
實作
 		報告
 		 
13 EFFICIENCY IN RULE-BASED LANGUAGES (I)( an example of knowledge engineering process) 1. The Rete Pattern-Matching Algorithm
2, The Pattern Network
3. The Join Network 		1. understand the internal architecture of Clips
2. understand how rules are internally reorganized in Rete network.
3. understand the pattern matching process.		 講授
實作
 		 
14 EFFICIENCY IN RULE-BASED LANGUAGES (II)( an example of knowledge engineering process) 1. The Rete Pattern-Matching Algorithm
2, The Pattern Network
3. The Join Network 		1. understand the internal architecture of Clips
2. understand how rules are internally reorganized in Rete network.
3. understand the pattern matching process.		 講授
實作
 		報告
 		 
15 DESIGN OF EXPERT SYSTEMS(the steps involved in knowledge engineering process) 1. Selecting the Appropriate Problem
2. Stages in the Development of the KE Process
3. The knowledge engineering process Life Cycle 		1. understand the the software engineering of the Development of an KE.
2. write a simple he KE Process		 講授
實作
 		問答
 		 
16 EXPERT SYSTEM DESIGN EXAMPLES (I) Demo ES/Applications of Expert Systems(the steps involved in knowledge engineering process) 1. Certainty Factors
2. Decision Trees
3. Backward Chaining
4. A Monitoring Problem
5. Steps Applications of the knowledge engineering process 		1. understand how to represent uncertainty in Clips.
2. understand how to reasoning with uncertainty.
3.understand how to support backward chaining.
54understand Steps Applications of the knowledge engineering process		 講授
實作
 		報告
 		 
17 彈性教學(線上專題報告) 彈性教學(線上專題報告):
1. Certainty Factors
2. Decision Trees
3. Backward Chaining
4. A Monitoring Problem
5.Advanced Topics of the KE 		彈性教學(線上專題報告):
1. understand how to represent uncertainty in Clips.
2. understand how to reasoning with uncertainty.
3 understand how to support backward chaining.
4. understand Advanced Topics of the KE		 心得發表
 		報告
 		彈性教學 (線上專題報告)  
18 彈性教學(線上專題報告) 彈性教學(線上專題報告):
1. Certainty Factors
2. Decision Trees
3. Backward Chaining
4. A Monitoring Problem
5.Advanced Topics of the KE 		彈性教學(線上專題報告):
1. understand how to represent uncertainty in Clips.
2. understand how to reasoning with uncertainty.
3.understand how to support backward chaining.
4. understand Advanced Topics of the KE		 心得發表
 		報告
彈性教學
 		彈性教學(線上專題報告)  
彈性教學週活動規劃
序
No.		 實施期間
Period		 實施方式
Content		 教學說明
Teaching instructions		 彈性教學評量方式
Evaluation		 備註
Notes
1 起:2024-12-31 迄:2025-01-07 5.小專題 Project 彈性教學(線上專題報告): 1. Certainty Factors 2. Decision Trees 3. Backward Chaining 4. A Monitoring Problem 5.Advanced Topics of the KE 彈性教學 (線上專題報告) 彈性教學 (線上專題報告)


教學要點概述：
1.自編教材 Handout by Instructor：
■ 1-1.簡報 Slids
■ 1-2.影音教材 Videos
□ 1-3.教具 Teaching Aids
■ 1-4.教科書 Textbook
■ 1-5.其他 Other
□ 2.自編評量工具/量表 Educational Assessment
□ 3.教科書作者提供 Textbook

成績考核 Performance Evaluation： 期末考：25%   期中考：25%   問答：10%   報告：15%   彈性教學：10%   平時考：15%  

教學資源(Teaching Resources)：
■ 教材電子檔(Soft Copy of the Handout or the Textbook)
□ 課程網站(Website)
扣考規定：https://curri.ttu.edu.tw/p/412-1033-1254.php