Knowledge Acquisition System Learning Objectives : Able to explain the knowledge transfer process Knowing about the knowledge acquisition Able to explain the role of knowledge engineer in the process of knowledge acquisition Knowing the difficulties in the knowledge acquisition process Know the methods in knowledge acquisition

1. Knowledge Acquisition
System
Chapter 3
Expert System

2. Objectives
 Able to explain the knowledge transfer process
 Knowing about the knowledge acquisition
 Able to explain the role of knowledge engineer in the
process of knowledge acquisition
 Knowing the difficulties in the knowledge acquisition
process
 Know the methods in knowledge acquisition

3. Architecture of Expert System

4. Expert System ?

5. Transferring Expertise
Objective of an expert system
 To transfer expertise from an expert to a
computer system and hen on to other humans
(no experts)
Activities
 Knowledge acquisition
 Knowledge representation
 Knowledge inference
 Knowledge transfer to the user
Knowledge is stored in a knowledge base

6.  Knowledge acquisition is a process of finding,
structuring and organizing clicking the knowledge
derived from the sources of knowledge and changed by
the knowledge engineering into the knowledge base, and
to the inference engine.
Knowledge Acquisition

7. Knowledge Engineer
 Helps the expert(s) structure the problem area by
interpreting and integrating human answers to
questions, drawing analogies, posing counterexamples,
and bringing to light conceptual difficulties
 Usually also the System Builder

8. Knowledge
source
Expert
Books
Document
documented
not documented
Knowledge
Acquisition
Knowledge
Engineering
K E
K E
D E
K E = Knowledge Engineer
D E = Domain Expert
Knowledge

9. Knowledge Engineer
Expert
Knowledge
Base
Knowledge
Engineer
(KE)
Elicitation Coding

10. Knowledge Engineering Process
Validasi
Pengetahuan
Sumber
Pengetahuan
Representasi
Pengetahuan
Basis
Pengetahuan
Justifikasi
Penjelasan
Inferensi
Akuisisi
Pengetahuan
Pengkodean

11. Difficulties in Knowledge Acquisition
 Expressing the knowledge
 Transfer to a Machine
 Number of participants
 Structuring the knowledge

12. Knowledge Acquisition Methods
 A human-intensive method for knowledge acquisition,
such as interviews and observations, used to elicit
knowledge from experts
Manual Methods

13. Knowledge Acquisition Methods
Expert
Knowledge
Base
Knowledge
Engineer
Documented
Knowledge
Coding
Elicitation
Manual Methods

14. Knowledge Acquisition Methods
 A knowledge acquisition method that uses computer-
based tools to support knowledge engineers in order to
facilitate the process
Semi Automatic Methods

15. Knowledge Acquisition Methods
Semi Automatic Methods
Expert
Knowledge
Base
Knowledge
Engineer
Computer
Aided
Interviewing
(interactive)
Coding

16. Knowledge Acquisition Methods
 An automatic knowledge acquisition method that
involves using computer software to automatically
discover knowledge from a set of data
Automatic Methods

17. Knowledge Acquisition Methods
Automatic Methods
Knowledge
Base
Induction
System
Case Histories
and Examples

18. Test your Knowledge Base
Criteria Explanation
Accuracy How good is the system to
reflect reality
Adaptability Possible improvements /
changes
Adequacy not deficient
Appeal Stimulate thinking and
practicality

19. Test your Knowledge Base
Criteria Explanation
Breadth How far the domain covered
Depth The degree of knowledge of
detail
Face validity Credibility knowledge
Generality Consistency advice, replication
system parameters

20. Test your Knowledge Base
Criteria Explanation
Precision Coverage in the knowledge
base
Realisms Resemblance to reality
Reliability Part expert system which was
empirically
Robustness Sensitivity conclusions on
capital structure

21. Test your Knowledge Base
Criteria Explanation
Sensitivity Impact of changes in the base
knowledge of output
The validity of the
technical and
operational
Goodness assumptions,
context, constraints,
conditions, and the impact to
the size actually
Turing test Ability evaluator on
conclusion

22. Test your Knowledge Base
Criteria Explanation
Usefulness How much is enough to solve
the problem
Validity The ability to generate
empirically correct production