1. Chapter 11 Knowledge Acquisition, Representation, and Reasoning
Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition
Chapter 11 Knowledge Acquisition, Representation, and Reasoning
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2. Learning Objectives Understand the nature of knowledge.
Learn the knowledge engineering processes.
Evaluate different approaches for knowledge acquisition.
Examine the pros and cons of different approaches.
Illustrate methods for knowledge verification and validation.
Examine inference strategies.
Understand certainty and uncertainty processing.
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3. Development of a Real-Time Knowledge-Based System at Eli Lilly Vignette
Problems with fermentation process
Quality parameters difficult to control
Many different employees doing same task
High turnover
Expert system used to capture knowledge
Expertise available 24 hours a day
Knowledge engineers developed system by:
Knowledge elicitation
Interviewing experts and creating knowledge bases
Knowledge fusion
Fusing individual knowledge bases
Coding knowledge base
Testing and evaluation of system
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4. Knowledge Engineering
Process of acquiring knowledge from experts and building knowledge base
Narrow perspective
Knowledge acquisition, representation, validation, inference, maintenance
Broad perspective
Process of developing and maintaining intelligent system
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5. Knowledge Engineering Process
Acquisition of knowledge
General knowledge or metaknowledge
From experts, books, documents, sensors, files
Knowledge representation
Organized knowledge
Knowledge validation and verification
Inferences
Software designed to pass statistical sample data to generalizations
Explanation and justification capabilities
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7. Knowledge Sources Documented Undocumented Acquired from
Written, viewed, sensory, behavior
Undocumented
Memory
Acquired from
Human senses
Machines
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8. Knowledge Levels Shallow Deep Surface level Input-output
Problem solving
Difficult to collect, validate
Interactions betwixt system components
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9. Knowledge Categories Declarative Procedural Metaknowledge
Descriptive representation
Procedural
How things work under different circumstances
How to use declarative knowledge
Problem solving
Metaknowledge
Knowledge about knowledge
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10. Knowledge Engineers Professionals who elicit knowledge from experts
Empathetic, patient
Broad range of understanding, capabilities
Integrate knowledge from various sources
Creates and edits code
Operates tools
Build knowledge base
Validates information
Trains users
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12. Elicitation Methods Manual Semiautomatic Automatic Based on interview
Track reasoning process
Observation
Semiautomatic
Build base with minimal help from knowledge engineer
Allows execution of routine tasks with minimal expert input
Automatic
Minimal input from both expert and knowledge engineer
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13. Manual Methods Interviews Structured Unstructured Semistructured
Goal-oriented
Walk through
Unstructured
Complex domains
Data unrelated and difficult to integrate
Semistructured
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14. Manual Methods Process tracking Protocol analysis Observation
Track reasoning processes
Protocol analysis
Document expert’s decision-making
Think aloud process
Observation
Motor movements
Eye movements
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15. Manual Methods Case analysis Critical incident User discussions
Expert commentary
Graphs and conceptual models
Brainstorming
Prototyping
Multidimensional scaling for distance matrix
Clustering of elements
Iterative performance review
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16. Semiautomatic Methods
Repertory grid analysis
Personal construct theory
Organized, perceptual model of expert’s knowledge
Expert identifies domain objects and their attributes
Expert determines characteristics and opposites for each attribute
Expert distinguishes between objects, creating a grid
Expert transfer system
Computer program that elicits information from experts
Rapid prototyping
Used to determine sufficiency of available knowledge
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18. Semiautomatic Methods, continued
Computer based tools features:
Ability to add knowledge to base
Ability to assess, refine knowledge
Visual modeling for construction of domain
Creation of decision trees and rules
Ability to analyze information flows
Integration tools
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19. Automatic Methods Data mining by computers
Inductive learning from existing recognized cases
Neural computing mimicking human brain
Genetic algorithms using natural selection
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20. Multiple Experts Scenarios Approaches Experts contribute individually
Primary expert’s information reviewed by secondary experts
Small group decision
Panels for verification and validation
Approaches
Consensus methods
Analytic approaches
Automation of process through software usage
Decomposition
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21. Automated Knowledge Acquisition
Induction
Activities
Training set with known outcomes
Creates rules for examples
Assesses new cases
Advantages
Limited application
Builder can be expert
Saves time, money
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22. Automated Knowledge Acquisition
Difficulties
Rules may be difficult to understand
Experts needed to select attributes
Algorithm-based search process produces fewer questions
Rule-based classification problems
Allows few attributes
Many examples needed
Examples must be cleansed
Limited to certainties
Examples may be insufficient
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23. Automated Knowledge Acquisition
Interactive induction
Incrementally induced knowledge
General models
Object Network
Based on interaction with expert
interviews
Computer supported
Induction tables
IF-THEN-ELSE rules
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24. Evaluation, Validation, Verification
Dynamic activities
Evaluation
Assess system’s overall value
Validation
Compares system’s performance to expert’s
Concordance and differences
Verification
Building and implementing system correctly
Can be automated
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26. Production Rules IF-THEN
Independent part, combined with other pieces, to produce better result
Model of human behavior
Examples
IF condition, THEN conclusion
Conclusion, IF condition
If condition, THEN conclusion1 (OR) ELSE conclusion2
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27. Artificial Intelligence Rules
Types
Knowledge rules
Declares facts and relationships
Stored in knowledge base
Inference
Given facts, advises how to proceed
Part of inference engines
Metarules
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28. Artificial Intelligence Rules
Advantages
Easy to understand, modify, maintain
Explanations are easy to get.
Rules are independent.
Modification and maintenance are relatively easy.
Uncertainty is easily combined with rules.
Limitations
Huge numbers may be required
Designers may force knowledge into rule-based entities
Systems may have search limitations; difficulties in evaluation
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29. Semantic Networks Graphical depictions Nodes and links
Hierarchical relationships between concepts
Reflects inheritance
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30. Frames All knowledge about object
Hierarchical structure allows for inheritance
Allows for diagnosis of knowledge independence
Object-oriented programming
Knowledge organized by characteristics and attributes
Slots
Subslots/facets
Parents are general attributes
Instantiated to children
Often combined with production rules
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31. Knowledge Relationship Representations
Decision tables
Spreadsheet format
All possible attributes compared to conclusions
Decision trees
Nodes and links
Knowledge diagramming
Computational logic
Propositional
True/false statement
Predicate logic
Variable functions applied to components of statements
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33. Reasoning Programs Inference Engine Rule interpreter Algorithms
Directs search of knowledge base
Forward chaining
Data driven
Start with information, draw conclusions
Backward chaining
Goal driven
Start with expectations, seek supporting evidence
Inference/goal tree
Schematic view of inference process
AND/OR/NOT nodes
Answers why and how
Rule interpreter
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34. Explanation Facility Justifier Types Makes system more understandable
Exposes shortcomings
Explains situations that the user did not anticipate
Satisfies user’s psychological and social needs
Clarifies underlying assumptions
Conducts sensitivity analysis
Types
Why
How
Journalism based
Who, what, where, when, why, how
Why not
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35. Generating Explanations
Static explanation
Preinsertion of text
Dynamic explanation
Reconstruction by rule evaluation
Tracing records or line of reasoning
Justification based on empirical associations
Strategic use of metaknowledge
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36. Uncertainty Widespread Important component Representation
Numeric scale
1 to 100
Graphical presentation
Bars, pie charts
Symbolic scales
Very likely to very unlikely
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37. Uncertainty Probability Ratio Bayes Theory Dempster-Shafer
Degree of confidence in conclusion
Chance of occurrence of event
Bayes Theory
Subjective probability for propositions
Imprecise
Combines values
Dempster-Shafer
Belief functions
Creates boundaries for assignments of probabilities
Assumes statistical independence
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38. Certainty Certainty factors Belief in event based on evidence
Belief and disbelief independent and not combinable
Certainty factors may be combined into one rule
Rules may be combined
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39. Expert System Development
Phases
Project initialization
Systems analysis and design
Prototyping
System development
Implementation
Postimplementation
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40. Project Initialization
Identify problems
Determine functional requirements
Evaluate solutions
Verify and justify requirements
Conduct feasibility study and cost-benefit analysis
Determine management issues
Select team
Project approval
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41. Systems Analysis and Design
Create conceptual system design
Determine development strategy
In house, outsource, mixed
Determine knowledge sources
Obtain cooperation of experts
Select development environment
Expert system shells
Programming languages
Hybrids with tools
General or domain specific shells
Domain specific tools
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42. Prototyping Rapid production Demonstration prototype
Small system or part of system
Iterative
Each iteration tested by users
Additional rules applied to later iterations
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43. System Development Development strategies formalized
Knowledge base developed
Interfaces created
System evaluated and improved
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44. Adoption strategies formulated System installed
Implementation
Adoption strategies formulated
System installed
All parts of system must be fully documented and security mechanisms employed
Field testing if it stands alone; otherwise, must be integrated
User approval
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45. Postimplementation Operation of system Maintenance plans
Review, revision of rules
Data integrity checks
Linking to databases
Upgrading and expansion
Periodic evaluation and testing
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46. Internet Facilitates knowledge acquisition and distribution
Problems with use of informal knowledge
Open knowledge source
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