WHAT I HAVE LEARNT (57033)

KMK3013 CONCEPT MAP OF CHAPTER 1-3

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WHAT I HAVE LEARNT (57033) by Mind Map: WHAT I HAVE LEARNT (57033)

1. Overview of Expert System

1.1. Artificial Intelligence

1.1.1. Intelligence performed by machine

1.1.2. designated to imitate human capabilities

1.1.2.1. Thinking

1.1.2.2. Sensing

1.2. Expert System (ES)

1.2.1. A computer system that emulates the decision-making ability of a human expert.

1.2.2. Knowledge

1.2.2.1. Collection of information used to solve problems

1.2.2.2. Such as facts, concepts, procedures, models, heuristics

1.3. Component of Expert System

1.3.1. Knowledge Base

1.3.1.1. Knowledge for understanding, formulate, and solve problems

1.3.1.2. eg: Facts and rules

1.3.1.3. Primary raw material of ES

1.3.2. Inference Engine

1.3.2.1. "Brain" of ES

1.3.2.2. Based on the use of rules

1.3.3. User Interface

1.3.3.1. Interaction among ES and users

1.3.3.2. Language processor

1.3.3.2.1. eg: NLP

1.3.4. Knowledge Acquisition

1.3.4.1. Accumulation, transfer, and transformation of problem-solving expertise from experts

1.3.4.2. Documented knowledge sources to computer program

1.3.4.3. Requires Knowledge Engineer (KE)

1.3.5. Knowledge Representation (KR)

1.3.5.1. Express information about the world in a form that can be utilize by computer system

1.3.5.2. If...., then.....

1.3.6. Experts

1.3.6.1. Experienced practioner

1.3.6.2. Make good decisions

1.3.6.3. Make quick decisions

1.4. Why ES/KBS

1.4.1. To achieve an expert knowledge

1.4.2. To disseminate knowledge

1.4.3. To ensure uniformity of advice or decisions

1.4.4. As basis for training other specialists

1.4.5. Advantages

1.4.5.1. Economical

1.4.5.2. Availability

1.4.5.3. Short Response time

1.4.5.4. Reliability

1.4.5.5. Explanation

1.4.5.6. Intellectual property

1.4.6. Disadvantages

1.4.6.1. High cost

1.4.6.2. Time consuming

1.4.6.3. High failure rate

1.5. Application of ES

1.5.1. DENDRAl

1.5.1.1. for chemistry field

1.5.2. MYCIN

1.5.2.1. for biology, specialized in bacteria and antibiotics

1.5.3. PROSPECTOR

1.5.3.1. for geological field

1.5.4. XCON/R I

1.5.4.1. For computer system

1.5.5. Medical diagnosis

1.5.6. Strategy games

1.5.6.1. Eg: Chess

1.5.7. Financial advice

2. Expert System Development Life Cycles

2.1. ES Development Team

2.1.1. Domain Expert

2.1.1.1. Person with knowledge and skills of solving problem in a specific area

2.1.1.2. Have major expertise in given domain

2.1.2. Project Manager

2.1.3. Knowledge Engineer

2.1.3.1. Able to design, build, and test ES

2.1.3.2. establishes reasoning methods being used by expert

2.1.4. Programmer

2.1.5. End user

2.2. ES Methodology

2.2.1. Cyclical Development

2.2.1.1. Rapid Application Development (RAD) Approach

2.2.1.2. Knowledge Acquisition-Prototype Development-Prototype Critiquing

2.2.1.3. Advantages:

2.2.1.3.1. show early progress in Knowledge elicitation task

2.2.1.3.2. generates enthusiasm in domain expert

2.2.2. Turban Expert System Development Life Cycle

2.2.2.1. Phase 1: Project Initialisation

2.2.2.2. Phase 2: System Analysis & Design

2.2.2.3. Phase 3: Prototyping

2.2.2.4. Phase 4: System Development

2.2.2.5. Phase 5: Implementation

2.2.2.6. Phase 6: Post-Implementation

2.2.3. Knowledge Acquisition and Documentation Structuring (KADS)

2.2.3.1. Planning

2.2.3.2. Knowledge Definition

2.2.3.3. Knowledge Design

2.2.3.4. Code & Checkout

2.2.3.5. Knowledge Verification

2.2.3.6. System Evaluation

3. Knowledge Acquisition

3.1. Scope & Types of Knowledge

3.1.1. Documented knowledge

3.1.2. Undocumented knowledge

3.1.3. Levels of Knowledge

3.1.3.1. Shallow

3.1.3.2. Deep

3.1.4. Categories of Knowledge

3.1.4.1. Declarative Knowledge

3.1.4.2. Procedural Knowledge

3.1.4.3. Meta Knowledge

3.2. Process of Knowledge Acquisition

3.2.1. Aims and objectives understood by KE

3.2.2. KE develops working knowledge

3.2.3. KE interacts with experts

3.2.3.1. Interviews

3.2.3.1.1. Structured

3.2.3.1.2. Focused

3.2.3.2. Meetings

3.2.4. KE produces document knowledge base

3.2.4.1. interview transcript

3.2.4.2. analysis of information

3.2.4.3. full description of major domain entities

3.3. Knowledge Acquisition Modelling Methods

3.3.1. Semiautomatic

3.3.1.1. little or no help from KE

3.3.1.2. minimal participant of experts

3.3.1.3. Knowledge acquisition can be supported by computer-based tools

3.3.1.3.1. PROTEGE-II

3.3.1.3.2. KSM

3.3.2. Automatic

3.3.2.1. roles of both expert and KE are minimize and eliminated

3.3.2.2. Knowledge Discovery

3.3.2.3. Reasons

3.3.2.3.1. Good KE are highly paid

3.3.2.3.2. Difficult to find good KE

3.3.2.3.3. Domain experts always busy

3.3.2.3.4. Uncooperative experts

3.3.3. Manual

3.3.3.1. Interviews

3.3.3.1.1. Structured

3.3.3.1.2. Focused

3.3.3.2. Observation

3.3.3.3. Discussions with users

3.3.3.4. Commentaries

3.3.3.5. Brainstorming

3.3.3.6. Prototyping

3.3.3.7. Conceptual graphs and models

3.3.3.8. Case Analysis

3.3.3.9. Critical incident analysis

3.4. Process Tracking Methods

3.4.1. a set of techniques attempt to track the reasoning process of expert

3.4.2. Protocol Analysis

3.4.2.1. KE acquires detailed knowledge from experts

3.4.2.2. Expert is asked to think aloud while performing task