1. Expert Systems

2. What is an Expert System?
A system that uses human expertise to make complicated decisions.
Simulates reasoning by applying knowledge and interfaces.
Uses expert’s knowledge as rules and data within the system.
Models the problem solving ability of a human expert.

3. Expert System
An expert system is a system that employs human knowledge captured in a computer to solve problems that ordinarily require human expertise.(Turban)
A computer program that emulates the behaviour of human experts who are solving real-world problems associated with a particular domain of knowledge. (Pigford & Braur)

4. Components of an Expert System
Knowledge
Base
User
User
Interface
Inference
Engine

5. 4 Components of an ES Knowledge Base Reasoning or Inference Engine
User Interface
Explanation Facility

6. Inference Engine
Asks the user questions about what they are looking for.
Applies the knowledge and the rules held in the knowledge base.
Appropriately uses this information to arrive at a decision.

7. User Interface Allows the expert system and the user to communicate.
Finds out what it is that the system needs to answer.
Sends the user questions or answers and receives their response.

8. Expert System Structure
Knowledge Based Rules
Expert
Interpreter
Inference
Engine
Natural
Language
Interface
Data base Context Set of facts
User

9. Knowledge Base
Represents all the data and information imputed by experts in the field.
Stores the data as a set of rules that the system must follow to make decisions.

10. Knowledge Acquisition
Expert System
Knowledge Engineer
Human Expert

11. Knowledge Acquisition
Knowledge acquisition is the process by which knowledge available in the world is transformed and transferred into a representation that can be used by an expert system. World knowledge can come from many sources and be represented in many forms.
Knowledge acquisition is a multifaceted problem that encompasses many of the technical problems of knowledge engineering, the enterprise of building knowledge base systems. (Gruber).

12. Knowledge Acquisition
Five stages:
Identification: - break problem into parts
Conceptualisation: identify concepts
Formalisation: representing knowledge
Implementation: programming
Testing: validity of knowledge

13. Early Expert Systems
DENDRAL – used in chemical mass spectroscopy to identify chemical constituents
MYCIN – medical diagnosis of illness
DIPMETER – geological data analysis for oil
PROSPECTOR – geological data analysis for minerals
XCON/R1 – configuring computer systems

14. Advantages of Expert Systems
Easy to develop and modify
The use of satisficing
The use of heuristics
Development by knowledge engineers and users

15. Applications of Expert Systems and Artificial Intelligence
Credit granting
Information management and retrieval
AI and expert systems embedded in products
Plant layout
Hospitals and medical facilities
Help desks and assistance
Employee performance evaluation
Loan analysis
Virus detection
Repair and maintenance
Shipping
Marketing
Warehouse optimization