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Unit 13: Expert System Architecture
13.8 Keywords Notes
Backward Chaining: It is an inference method that can be described (in lay terms) as working
backward from the goals.
Baye’s Theorem: It provides us with a statistical theory of evidence which allows us to update the
probabilities attached to certain facts in the light of new evidence. The fundamental notion is
that of conditional probability:
Expert Systems: It is a computer system that emulates the decision-making ability of a human
expert.
Inference Engine: It is computer program that tries to derive answers from a knowledge-base. It
is the “brain” that expert systems use to reason about the information in the knowledge-base for
the ultimate purpose of formulating new conclusions.
Knowledge Acquisition: It is the transformation of knowledge from the form in that it is exist in
the world into the form that it can be used by expertise systems.
Knowledge Engineering: It is an engineering discipline that involves integrating knowledge
into computer systems in order to solve complex problems normally requiring a high level of
human expertise.
13.9 Review Questions
1. Why do we need knowledge engineers to develop expert systems? Can we ask experts to
develop the system by themselves?
2. What do you think of their approach, which developed ten separate knowledge bases and
then put them together through knowledge fusion? What are the pros and cons of this
approach?
3. For systems testing and evaluation, they used simulated data rather than real data. Why is
this acceptable for real-time expert systems? Can you think of a better approach for system
testing and evaluation?
4. What are the benefits of using knowledge acquisition tools?
5. What are the steps in the knowledge engineering process?
6. Describe the evaluation, validation, and verification of knowledge.
Answers: Self Assessment
1. False 2. True
3. True 4. False
5. False 6. True
7. True 8. False
9. True 10. True
11. True 12. True
13. False 14. True
15. False 16. False
17. True 18. False
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