1. Chapter 11 Expert system architecture, representation of knowledge, Knowledge Acquisition, and Reasoning

2. Introduction to Knowledge Management
Knowledge is information that is contextual, relevant, and actionable.

3. Introduction to Knowledge Management

4. Knowledge Engineering
Knowledge Engineering is a Process of acquiring knowledge from experts and building knowledge base
There are two Knowledge Engineering perspectives
Narrow perspective
Knowledge acquisition, representation, validation, inference, maintenance
Broad perspective
Process of developing and maintaining intelligent system
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

5. Knowledge Engineering Process
The KE steps are:
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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

6. Knowledge Engineering Process
Inference
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

7. Knowledge Sources Knowledge Sources are Documented Undocumented
Written, viewed, sensory, behavior
Undocumented
Memory
Acquired from
Human senses
Machines
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

8. Knowledge levels Knowledge Levels are: Shallow Deep Surface level
Input-output
Deep
Problem solving
Difficult to collect, validate
Interactions between system components
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

9. Knowledge categories Knowledge Categories Declarative Procedural
Descriptive representation
Procedural
How things work under different circumstances
How to use declarative knowledge
Problem solving
Metaknowledge
Knowledge about knowledge
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

11. Knowledge Engineers
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

12. Knowledge Management Activities
Knowledge management initiatives and activities
Most knowledge management initiatives have one of three aims:
To make knowledge visible
To develop a knowledge-intensive culture
To build a knowledge infrastructure

13. 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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

14. Manual Methods Interviews Structured Unstructured Semistructured
Goal-oriented
Unstructured
Complex domains
Data unrelated and difficult to integrate
Semistructured
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

15. Manual Methods
Process tracking: is a set of techniques to track reasoning processes
Protocol analysis
Document expert’s decision-making
Think aloud process
Observation
Motor movements
Eye movements
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

16. 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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

17. 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 (traits) and opposites for each attribute
Expert distinguishes between objects using numerica scale e.g.
Create a grid
Expert transfer system (ETS)
Computer program that interviews experts to elicit information
Rapid prototyping used to determine sufficiency of available knowledge
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

18. Repertory grid analysis
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

19. 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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

20. Automatic Methods Knowledge discovery by computers
Inductive learning from existing recognized cases
Neural computing mimicking human brain
Genetic algorithms using natural selection
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

21. 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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

22. Automated Knowledge Acquisition
Rule Induction
Training set with known outcomes
Creates rules, for example:
If income is $70,000 or more, approve the loan.
Assesses new cases
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

23. Automated Knowledge Acquisition
Advantages
Complex problem domain
Builder can be expert
Saves time, money

24. Automated Knowledge Acquisition
Difficulties
Rules may be difficult to understand
Experts needed to select attributes
Algorithms are based on search process that produces fewer questions
Rule-based classification problems
Allows few attributes
Many examples needed
Examples must be cleansed
Limited to certainties
Examples may be insufficient
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

25. 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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

26. 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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

27. Representation of Knowledge 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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

28. Artificial Intelligence Rules Common types
Knowledge rules
Declares facts and relationships
Stored in knowledge base
Inference
Given facts, advises how to proceed
Part of inference engines
Metarules
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

29. Artificial Intelligence Rules Pros and cons
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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

30. Representation of Knowledge Semantic Networks
Graphical depictions
Nodes and links
Hierarchical relationships between concepts
Reflects inheritance e.g. does Sam need food?
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

31. Representation of Knowledge 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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

32. 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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

33. Reasoning In Rule-Based Systems
Inference Engine
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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

34. Explanation Facility
Explanation facility is:The part of an ES that provides explanations has several purposes:
Justifier
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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang