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Chapters 11 & 12, 13: Knowledge Acquisition, Representation and Validation   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The Solution ,[object Object],[object Object],[object Object],[object Object]
Knowledge Engineering  ,[object Object],[object Object],[object Object]
Knowledge Engineering Process Activities ,[object Object],[object Object],[object Object],[object Object],[object Object]
Knowledge Acquisition  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Difficulties in Knowledge Acquisition  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Other Reasons
Overcoming the Difficulties  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Required Skills and Characteristics of Knowledge Engineers  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Methods of Knowledge Acquisition: An Overview   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Recommendation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Tracking Methods  ,[object Object],[object Object],[object Object],[object Object]
Protocol Analysis ,[object Object],[object Object]
 
Observations and Other Manual Methods  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Expert-driven Methods  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Manual Method: Expert's Self-reports  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Machine Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Computer-aided Knowledge Acquisition, or Automated Knowledge Acquisition Objectives ,[object Object],[object Object],[object Object],[object Object],[object Object]
Automated Knowledge Acquisition (Machine Learning) ,[object Object],[object Object],[object Object],[object Object]
Machine Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Automated Rule Induction  ,[object Object],[object Object],[object Object]
 
 
Case-based Reasoning (CBR) ,[object Object],[object Object],[object Object],[object Object],Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Finding Relevant Cases Involves ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ
CBR Construction -  Special Tools - Examples ,[object Object],[object Object],[object Object],Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Example:  College Major Advisor ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
APTITUDE INTERESTS FINANCIAL NEED RECOMMENDATION OF A MAJOR College Major Advisor: Initial Block Diagram
APTITUDE INTERESTS FINANCIAL NEED RECOMMENDATION OF A MAJOR College Major Advisor: Expanded Block Diagram math skills prog. skills manual dexterity computers problem solving repair things desk vs. field job at graduation
math? (yes, no) programming (yes, no) manual dexterity (yes, no) computers (yes, no) problem solving (yes, no) place (desk, field) repair (yes, no) finance (yes, no) APTITUDE INTERESTS SUGGESTED MAJOR College Major Advisor: Dependency Diagram
Induction Table Example ,[object Object],[object Object]
 
[object Object],[object Object],[object Object],[object Object],[object Object]
Knowledge Representation ,[object Object],[object Object],[object Object]
Major Advantages of Rules  ,[object Object],[object Object],[object Object],[object Object],[object Object]
Production Rules: Drawbacks ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Frames ,[object Object],[object Object],[object Object],[object Object],[object Object]
KR With Frames ANIMALS MAMMALS REPTILES BIRDS FISH DOG HUMAN WHALE PENGUIN LONG JOHN SILVER Reproduce: Yes Life-Form: Yes Scales: Yes Warmblooded: No Warmblooded: Yes Spinal Cord: Yes Legs: 4 Legs: 2 Legs: 1 Legs: 0 Fins: Yes Legs: 2 Wings: Yes Feathers: Yes Feathers: No Scales: Yes Fins: yes
Semantic Networks ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
KR With Semantic Networks LIGHT WORKING HEADLIGHT FUNCTIONAL LIGHT BULB FUNCTIONAL CIRCUIT IS-A HAS-A HAS-A
Semantic Networks ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
KR with Multiple Schemes IF Battery-Age < 5 and Battery_terminals are Uncorroded THEN Battery can work If Battery can work and Fuse is Functional and Wiring is Functional THEN Electric Circuit can work ELECTRIC SUBSYSTEM IGNITION HEADLIGHT LIGHT BULB ELECTRIC CIRCUIT Working Head Light Functional Light Bulb Functional Circuit Working Battery Functional Fuse Functional Wiring 5-Years Old Uncorroded Terminals HAS-A HAS-A HAS-A HAS-A NEEDS-A HAS-A NEEDS-A
Choosing a Representation Scheme ,[object Object],[object Object],[object Object],[object Object],[object Object]
Validation & Verification of the Knowledge Base  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
 
Method for Validating ES ,[object Object],[object Object],[object Object],[object Object],[object Object]
ES Elements to be Validated ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Inferencing Methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Example Variables: A = have $10,000 B = younger than 30 C = education at college level D = annual income > = $40,000 E = invest in securities F = invest in growth stocks G = invest in IBM stocks Rules: R1: if A and C then E R2: if D and C then F R3: if B and E then F R4: if B then C R5: if F then G Initial Facts: A and B
Inferencing Methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Inferencing with Uncertainty   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Theory of Certainty (Certainty Factors)   ,[object Object],[object Object],[object Object],[object Object],Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Calculating Confidence Factors ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
AND ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],OR Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Combining Two or More Rules ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Assume an independent relationship between the rules   ,[object Object],[object Object],[object Object],Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ

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helbredte

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  • 23.  
  • 24.  
  • 25.
  • 26.
  • 27.
  • 28.
  • 29. APTITUDE INTERESTS FINANCIAL NEED RECOMMENDATION OF A MAJOR College Major Advisor: Initial Block Diagram
  • 30. APTITUDE INTERESTS FINANCIAL NEED RECOMMENDATION OF A MAJOR College Major Advisor: Expanded Block Diagram math skills prog. skills manual dexterity computers problem solving repair things desk vs. field job at graduation
  • 31. math? (yes, no) programming (yes, no) manual dexterity (yes, no) computers (yes, no) problem solving (yes, no) place (desk, field) repair (yes, no) finance (yes, no) APTITUDE INTERESTS SUGGESTED MAJOR College Major Advisor: Dependency Diagram
  • 32.
  • 33.  
  • 34.
  • 35.
  • 36.
  • 37.
  • 38.
  • 39. KR With Frames ANIMALS MAMMALS REPTILES BIRDS FISH DOG HUMAN WHALE PENGUIN LONG JOHN SILVER Reproduce: Yes Life-Form: Yes Scales: Yes Warmblooded: No Warmblooded: Yes Spinal Cord: Yes Legs: 4 Legs: 2 Legs: 1 Legs: 0 Fins: Yes Legs: 2 Wings: Yes Feathers: Yes Feathers: No Scales: Yes Fins: yes
  • 40.
  • 41. KR With Semantic Networks LIGHT WORKING HEADLIGHT FUNCTIONAL LIGHT BULB FUNCTIONAL CIRCUIT IS-A HAS-A HAS-A
  • 42.
  • 43. KR with Multiple Schemes IF Battery-Age < 5 and Battery_terminals are Uncorroded THEN Battery can work If Battery can work and Fuse is Functional and Wiring is Functional THEN Electric Circuit can work ELECTRIC SUBSYSTEM IGNITION HEADLIGHT LIGHT BULB ELECTRIC CIRCUIT Working Head Light Functional Light Bulb Functional Circuit Working Battery Functional Fuse Functional Wiring 5-Years Old Uncorroded Terminals HAS-A HAS-A HAS-A HAS-A NEEDS-A HAS-A NEEDS-A
  • 44.
  • 45.
  • 46.  
  • 47.
  • 48.
  • 49.
  • 50. Example Variables: A = have $10,000 B = younger than 30 C = education at college level D = annual income > = $40,000 E = invest in securities F = invest in growth stocks G = invest in IBM stocks Rules: R1: if A and C then E R2: if D and C then F R3: if B and E then F R4: if B then C R5: if F then G Initial Facts: A and B
  • 51.
  • 52.
  • 53.
  • 54.
  • 55.
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  • 59.