B. Ross Cosc 4f79 1 Knowledge acquisition (Ch. 17 Durkin) • knowledge engineering: building expert systems • knowledge acquisition: process of extracting knowledge from an expert, organizing it, and encoding it into a knowledge base • knowledge elicitation: extracting knowledge from an expert • knowledge acquisition is the principle bottleneck in expert system development • many techniques and theories about how to best do this • more tools are appearing to help in this – early example: inductive inference tables • active research area – psychologists are especially interested in elicitation issues, as it is a fundamental problem of human psychology
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B. Ross Cosc 4f79 1 Knowledge acquisition (Ch. 17 Durkin) knowledge engineering: building expert systems knowledge acquisition: process of extracting knowledge.
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B. Ross Cosc 4f79 1
Knowledge acquisition (Ch. 17 Durkin)
• knowledge engineering: building expert systems
• knowledge acquisition: process of extracting knowledge from an expert, organizing it, and encoding it into a knowledge base
• knowledge elicitation: extracting knowledge from an expert
• knowledge acquisition is the principle bottleneck in expert system development
• many techniques and theories about how to best do this
• more tools are appearing to help in this
– early example: inductive inference tables
• active research area
– psychologists are especially interested in elicitation issues, as it is a fundamental problem of human psychology
B. Ross Cosc 4f79 2
Knowledge acquisition
Expert
End user
Knowledgeengineer
KNOWLEDGEBASE
Formalizedstructuredknowledge
knowledgeconceptssolutions
data, problems, questions
Prototypes,needs queries
Needs,usability,feedback
Also: other experts, literature
B. Ross Cosc 4f79 3
Some problematic phenomena
1. Paradox of expertise: The more competent a domain expert is, the less able she is to describe the knowledge they use to solve problems.
- studies & experience shows that experts are experts because they compile their vast knowledge into compact, efficiently retrievable form
- as a result, they ignore lots of details about how they derive conclusions --> intuition is prevalent; structured principles are ignored
- for example, experts use lots of generalization and pattern matching to solve standard and new problems
2. Experts make bad knowledge engineers
- domain experts are the worst people for formalizing their own knowledge - non-objective, unfamiliar with AI technology, ...
- need an objective view of knowledge, which isn’t possible from expert
- eg. try to formalize how you go about creating a computer program to solve some problem
B. Ross Cosc 4f79 4
Some problematic phenomena
3. Don't believe everything experts say.
• experts rely on intuition, compiled knowledge • unaware of the deep reasoning; use shallow reasoning
ie. often short-term memory isn’t used;rather, long-term memory as obtained via past experiences is relied upon ---> huge gaps in knowledge
• because experts don't know the formal structure of their knowledge, their descriptions will likely be wrong
- they aren’t used to verbalizing their expertise!
• therefore, knowledge engineer must watch for knowledge that is... - irrelevant, incomplete, incorrect, inconsistent
- knowledge engineer will formalize an expert's knowledge, and then test it to see whether it is logically consistent
B. Ross Cosc 4f79 5
Steps in knowledge acquisition
1. Collect: (elicitation)
- getting the knowledge out of the expert
- most difficult step
- lots of strategies
2. Interpret:
- review collected knowledge, organize, filter
3. Analyze:
- determining types of knowledge, conceptual relationships