1. Intro to Expert Systems Paula Matuszek CSC 8750, Fall, 2004
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Expert Systems, Paula Matuszek

2. What is an Expert System?
A Branch of Artificial Intelligence
An expert system is
An intelligent computer program
That uses knowledge
And automated reasoning or inference
To solve difficult problems
By emulating human expertise
In a specialized or delimited area
Overlaps with knowledge-based systems
May provide solutions, take actions, give advice
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Expert Systems, Paula Matuszek

3. Expert Systems, Paula Matuszek
How did we get here?
Early AI programs
Powerful symbolic programming
Applied to small amounts of data
Second stage: heuristics
Hardware advances: more memory available
Cognitive science research in nature of expertise
Expertise = knowledge
Dendral
Mycin
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Expert Systems, Paula Matuszek

4. Elements of an Expert System
Knowledge Base
Inference
Fact/Database
Optional but common components
User interface
Explanation facility
Automated Knowledge acquisition facility
Other bells and whistles
An expert system shell is a system for creating expert systems, typically containing all of the above except the KB
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Expert Systems, Paula Matuszek

5. Expert Systems, Paula Matuszek
Knowledge Base (KB)
Information derived from the human expert: rules of thumb, in-depth knowledge about overall domain.
Knowledge about a field represented in an organized, declarative fashion
Each application of an expert system has a KB
Process of creating KB is knowledge engineering (KE)
KE includes :
Decisions about What information to represent
Decisions about How to represent it
Encoding detailed knowledge
Roles in KE include
Expert system specialist (knowledge engineer)
Subject matter specialist (domain expert)
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Expert Systems, Paula Matuszek

6. Expert Systems, Paula Matuszek
Inference Engine
Reasoning tool of the expert system.
Inference engine
Applies the contents of the knowledge base
To the information in a fact base
To reach a decision.
Inference typically proceeds in one of two ways:
Start with known facts and work forward (forward chaining)
Start with a goal and work backward (backward chaining)
Form of inference is related to form of knowledge base.
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Expert Systems, Paula Matuszek

7. Expert Systems, Paula Matuszek
Fact Base
Information to this specific case or run of the expert system.
Also known as database, blackboard, instances
Typically empty at the beginning of a case.
Gradually instantiated as a case is processed
May include information such as
Details about case
Catalog/price information
Configuration or location information
Intermediate or temporary information used by inference mechanism
Often partially instantiated automatically from a database
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Expert Systems, Paula Matuszek

8. Typical Expert Systems Applications
Maintenance and diagnostics
Configuration and design
Advising
Instruction
Interpretation
Monitoring and control, often real-time
Planning
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Expert Systems, Paula Matuszek

9. Some Advantages of Expert Systems
Availability
Available 24/7
Available in inaccessible or dangerous locations
Consistency and reliability
Speed of response
Cost
Replace human experts with lower-level personnel
Replace human experts with machine contacts
Permanence (capture knowledge)
Easy record keeping, links to dbs, explanations of decisions, other uses for knowledge once captured
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Expert Systems, Paula Matuszek

10. What does building an expert system involve?
Scope the problem domain
Choose appropriate knowledge representation for the problem
Choose appropriate inference for KR and problem
Implement inference or choose tool/shell
Create knowledge base
Iterate: test and modify
Add bells and whistles
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Expert Systems, Paula Matuszek

11. Characteristics of an Expert System
High performance: as good as human expert
Adequate response time
Reliability: doesn’t crash, fails softly
Understandable:
Builds confidence in decisions
Verifies the knowledge
Improves knowledge of users
Flexible
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Expert Systems, Paula Matuszek

12. Good Problems for Expert Systems
Theoretical issues:
Domain is well-defined and delimited
Experts exist, and expertise can be taught
Problem solving does not have heavy dependency on common sense
Information needed can be input readily
Typically, heuristic domains with a lot of uncertainty
Practical issues:
Enough use to justify cost of building and maintaining
Experts are co-operative
Experts are rare, inaccessible, expensive, overworked
Other more appropriate solutions don’t exist
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Expert Systems, Paula Matuszek

13. Expert Systems, Paula Matuszek
Red Tape and Process
Information will be posted at
Overview and text
Syllabus
Requirements
Academic Integrity
CLIPS
Version 6.05 on CD
Version 6.2 can be downloaded from download site
11/18/2018
Expert Systems, Paula Matuszek