Expert Systems Reasonable Reasoning

Expert Systems Reasonable Reasoning

• An Ad Hoc approach

Problems with Logical Reasoning

• Brittleness: one axiom/fact wrong, system can prove anything (Correctness)
• Large proof spaces (Efficiency)
• “Probably” not representable (Representation)
• No notion of combining evidence
• Doesn’t provide confidence in conclusion.

Lecture Goals

• What is an expert system?
• How can they be built?
• When are they useful?
• General Architecture
• Validation:
• How to know if they are right

Class News

• Quiz dates and homework dates updated:
• Extra day for probability hwk/quiz
• All homeworks must be typed

Expert System

• Attempt to model expert decision making in a limited domain
• Examples: medical diagnosis, computer configuration, machine fault diagnosis
• Requires a willing Expert
• Requires knowledge representable as rules
• Doesn’t work for chess
• Preponderance of evidence for decision, not proof. (Civil law suits)

Why Bother?

• Reproduce Expertise: make available
• Record Expertise: experts die
• Combine expertise of many
• Didn’t work
• Teach expertise
• Didn’t work
• Expand application area of computers
• What tasks can computers do?

Architecture

• Domain Knowledge as “if-then” rules
• Inference Engine
• Backward chaining
• Forward chaining
• Calculus for combining evidence
• Construct all proofs, not just one
• Explanation Facility: can answer “why?”

MYCIN: 1972-1980

• 50-500 rules, acquired from expert by interviewing. Blood disease diagnosis.
• Example rule:
• if stain of organism is gramneg and morphology is rod and aerobicity is aerobic then strongly suggestive (.8) that organism is enterocabateriacease.
• Rules matched well knowledge in domain: medical papers often present a few rules
• Rule = nugget of independent knowledge