1. 전문가 시스템(Expert Systems)
특정의 문제를 해결하기 위해 특정의 전문적인 지식을 기반으로 실행되는 컴퓨터 시스템
특정 문제 영역에서 그 영역의 인간 전문가가 의사 결정을 내리는 것과 유사하게 동작하는 컴퓨터 시스템
인공지능 분야에서 상업적으로 성공한 대표적 분야
기능적 측면에서 필요한 요소
추론 기능
사용자-시스템 간의 상호작용에 의한 데이터 획득
결론의 정당성(Justification)
모듈 구조(Modular architecture)

2. 전문가 시스템의 배경 high 매우 특화된 프로그램을 생성하기 위해 좁은 문제 영역에 대한 고수준의 정제된 지식을 이용
PROGRAM POWER
표현 및 탐색 기능을
향상시키는 일반 방법론을
찾고, 특화된 프로그램을
구축하고자 함
문제해결을 위한 일반
방법론을 찾고, 범용-목적
프로그램을 구축하고자 함
low
1960
1970
1980

3. Building Expert Systems
Breakthrough
To make a program intelligent, provide it with lots of high-quality, specific knowledge about some problem area
Building Expert Systems
Knowledge Engineering: The process of building an expert system
Knowledge Engineer: Expert-system builder
Need the interaction between the KE and human experts
The KE extracts procedures, strategies, and rules of thumb for problem solving
The KE builds this knowledge into the expert system
Result: a program that solves problems like the human experts
Queries, Problems
Domain
Expert
Knowledge
Engineer
Expert
System
Strategies,
Rules-of-thumb,
Domain rules
Answers, Solutions

4. 전문가 시스템 구축과정 Toolbuilder Domain Expert Extends and tests Builds
Interviews
Expert
System
Building
Tool
Knowledge
Engineer
End-User
Expert
System
Uses
Builds,
refines,
and tests
Uses
Adds
data
Clerical
Staff

5. Basic Components of Expert System (ES)
Experts/Knowledge Engineer
Knowledge Base
Inference Engine
User Interface

6. Basic Concepts of Expert Systems - cont.
Recognizing and formulating the problem
Solving the problem quickly and properly
Explaining the solution
Learning from experience
Restructuring knowledge
Determining relevance
Expertise is the extensive, task-specific knowledge acquired from training, reading and experience
Theories about the problem area
Hard-and-fast rules and procedures
Rules (heuristics)
Global strategies
Meta-knowledge (knowledge about knowledge)
Facts

7. Basic Concepts of Expert Systems - cont.
Transferring Expertise
Objective of an expert system
To transfer expertise from an expert to a computer system and
Then on to other humans (non-experts)
Activities
Knowledge acquisition
Knowledge representation
Knowledge inference
Knowledge transfer to the user
Knowledge is stored in a knowledge base
Two knowledge types
Fact / Procedures(rules)

8. The Knowledge Engineer
Helps the expert(s) structure the problem area by interpreting and integrating human answers to questions, drawing analogies, posing counterexamples, and bringing to light conceptual difficulties
Knowledge Acquisition Subsystem
Knowledge acquisition is the accumulation, transfer and transformation of problem-solving expertise from experts and/or documented knowledge sources to a computer program for constructing or expanding the knowledge base
Usually also the System Builder

9. The User Possible Classes of Users
A non-expert client seeking direct advice - the ES acts as a Consultant or Advisor
A student who wants to learn - an Instructor
An ES builder improving or increasing the knowledge base - a Partner
An expert - a Colleague or Assistant
The Expert and the Knowledge Engineer Should Anticipate Users' Needs and Limitations When Designing ES

10. Knowledge Base
The knowledge base contains the knowledge necessary for understanding, formulating, and solving problems
Two Basic Knowledge Base Elements
Facts
Special heuristics, or rules that direct the use of knowledge
Knowledge is the primary raw material of ES
Incorporated knowledge representation

11. Inference Engine The brain of the ES
The control structure or the rule interpreter
Provides a methodology for reasoning
The computer is programmed so that it can make inferences
Major Element
Interpreter
Scheduler
Consistency Enforcer

12. User Interface
Language processor for friendly, problem-oriented communication
NLP, or menus and graphics
Explanation subsystem
Traces responsibility and explains the ES behavior by interactively answering questions
Why?
How?
What?
(Where? When? Who?)
Knowledge Refining System
Learning for improving performance

13. Expert System Building Tool(not the expert system)
Building Language
Expert
System
Support
Environment
User

14. What Good Are Expert Systems
Why not use real Experts?
→ Why do we develop expert systems
Comparing human and artificial expertise: the good news
The Good News
Human Expertise Artificial Expertise
Perishable Permanent
Difficult to transfer Easy to transfer
Difficult to document Easy to document
Unpredictable Consistent
Expensive Affordable

15. Why keep a human in the loop?
Why not eliminate human expert replacing them with expert systems?
Comparing human and artificial expertise: the bad news
The Bad News
Human Expertise Artificial Expertise
Creative Uninspired
Adaptive Needs to be told
Sensory experience Symbolic input
Broad focus Narrow focus
Commonsense knowledge Technical knowledge

16. How Are Expert Systems Organized?
Organizing Knowledge
Knowledge: The information a computer program needs with which it can behave intelligently 
take the form of facts and rules
sometimes considers uncertainty
Many of rules in expert systems are heuristics
Comparing Algorithmic with Heuristic methods

17. (General Problem-Solving
The structure of an Expert System
Expert System
Knowledge Base
(Domain Knowledge)
Knowledge
Representations
Facts
Rules
Interpreter
Scheduler
Inference Engine
(General Problem-Solving
Knowledge)

18. Knowledge-Based Systems
Artificial
Intelligence
Programs
Exhibit intelligent
behavior by skillful
application of heuristics
Make domain knowledge
explicit and separate from
the rest of the system
Knowledge-based
Systems
Apply expert knowledge
to difficult, real world
problems
Expert
Systems

19. How Do Expert Systems Differ from Conventional Programs
Comparison of data processing and knowledge engineering
Date Processing Knowledge Engineering
Representations and use Representation and use of
of data knowledge
Algorithmic Heuristic
Repetitive Process Inferential process
Effective manipulation Effective manipulation of large
of large data bases knowledge bases

20. Basic Characteristics of an Expert Systems
Characteristics of an expert system that distinguish it from a conventional program
Expert Systems sometimes make mistakes
Expert System
Exhibit expert performance
Expertise
Have high level of skill
Have adequate robustness
Represent knowledge symbolically
Symbolic reasoning
Reformulate symbolic knowledge
Handle difficult problem domains
Depth
Use complex rules
Examine its own reasoning
Self-Knowledge
Explain its operation

21. What Have Expert Systems Been Used For?
Basic Activities of Expert Systems
Generic Categories
Category Problem Addressed
Interpretation Inferring situation descriptions from sensor data
Prediction Inferring likely consequences of given situations
Diagnosis Inferring system malfunctions from observables
Design Configuring objects under constraints
Planning Designing actions
Monitoring Comparing observations to expected outcomes
Debugging Prescribing remedies for malfunctions
Repair Executing plans to administer prescribed remedies
Instruction Diagnosing, debugging, and repairing user
behavior
Control Governing overall system behavior

22. Application Areas for Expert systems
Chemistry
DENDRAL, MOLGEN
Computer Systems
XCON
Electronics
ACE, EURISKO, SOPHIE
Geology
PROSPECTOR
Medicine
MYCIN, ONCOCIN, GUIDON
Military
Engineering