1. Knowledge-Based Systems in Business Workshop PAIW-April 2003
Jay E. Aronson
Professor of Management Information Systems
AI Faculty Fellow
Director: Master of Internet Technology Program
College of Business
706/
ebiz.terry.uga.edu (MIT Program)
Jay E. Aronson: KBS inBusiness Workshop: PAIW April 2003

2. Material Adapted From
Turban, Efraim, and Jay E. Aronson, Decision Support Systems and Intelligent Systems, Prentice Hall, Upper Saddle River, NJ, 6th edition, 2001/7th edition, 2004.

3. Outline Artificial intelligence Expert system/knowledge-based systems
Knowledge Engineering
Knowledge Acquisition
Knowledge Representation
Inferencing
Expert Systems Practicum
Intelligent Systems Development

4. Artificial Intelligence (Simple Definition)
Behavior by a machine that, if performed by a human being, would be called intelligent

5. Make machines more useful (entrepreneurial purpose)
AI Objectives
Make machines smarter (primary goal)
Understand what intelligence is (Nobel Laureate purpose)
Make machines more useful (entrepreneurial purpose)

6. AI Represents Knowledge as Sets of Symbols
A symbol is a string of characters that stands for some real-world concept
Examples
Product
Defendant
0.8
Chocolate

7. How AI Works AI Programs Manipulate Symbols to Solve Problems
Symbols and Symbol Structures Form Knowledge Representation
Artificial Intelligence Dealings Primarily with Symbolic, Nonalgorithmic Problem Solving Methods

8. Some Major AI Areas Expert Systems Natural Language Processing
Speech Understanding
(Smart) Robotics and Sensory Systems
Neural Computing
Fuzzy Logic
Genetic Algorithms
Intelligent Software Agents

9. Expert Systems/Knowledge-Based Systems
Attempt to Imitate Expert Reasoning Processes and Knowledge in Solving Specific Problems
Most Popular Applied AI Technology
Enhance Productivity
Augment Work Forces
Narrow Problem-Solving Domain or Tasks
Qualitative Problem-Solving Aspects

10. Expert Systems Provide Direct Application of Expertise
Expert Systems Do Not Replace Experts, But
Makes their Knowledge and Experience More Widely Available
Permits Non Experts to Work Better

11. Expert Systems Expertise Transferring Expertise Inferencing Rules
Explanation Capability

12. Human Expert Behaviors
Recognize and formulating the problem
Solve problems quickly and properly
Explain the solution
Learn from experience
Restructure knowledge
Break rules
Determine relevance
Degrade gracefully

13. Transferring Expertise
Objective of an expert system
To transfer expertise from an expert to a computer system and
Then on to other humans (nonexperts)
Activities
Knowledge acquisition
Knowledge representation
Knowledge inferencing
Knowledge transfer to the user
Knowledge is stored in a knowledge base

14. Inferencing Reasoning (Thinking)
The computer is programmed so that it can make inferences
Performed by the Inference Engine

15. Rules IF-THEN-ELSE Explanation Capability
By the justifier, or explanation subsystem
ES versus Conventional Systems

16. Structure of Expert Systems
Development Environment
Consultation (Runtime) Environment

17. Three Major ES Components
User Interface
Inference
Engine
Knowledge
Base

18. All ES Components Knowledge Acquisition Subsystem Knowledge Base
Inference Engine
User Interface
Blackboard (Workplace)
Explanation Subsystem (Justifier)
Knowledge Refining System
User
Most ES do not have a Knowledge Refinement Component

19. Knowledge Base
The knowledge base contains the knowledge necessary for understanding, formulating, and solving problems
Two Basic Knowledge Base Elements
Facts
Special heuristics, or rules that direct the use of knowledge
Knowledge is the primary raw material of ES
Incorporated knowledge representation

20. Inference Engine The brain of the ES
The control structure (rule interpreter)
Provides methodology for reasoning

21. User Interface
Language processor for friendly, problem-oriented communication
NLP, or menus and graphics

22. The Human Element in Expert Systems
Knowledge Engineer
User
Others

23. How Expert Systems Work
Major Activities of
ES Implementation and Use
Development
Consultation
Improvement

24. ES Shell Includes All Generic ES Components But No Knowledge
EMYCIN from MYCIN
(E=Empty)

25. Expert Systems (Rule-Based) Shells/Software Development Packages
Corvid Exsys
K-Vision
KnowledgePro
XpertRule KBS G2
Guru
CLIPS
JESS
Many More: Free and Costly

26. Problem Areas Addressed by Expert Systems
Interpretation systems
Prediction systems
Diagnostic systems
Design systems
Planning systems
Monitoring systems
Debugging systems
Repair systems
Instruction systems
Control systems

27. Expert Systems Benefits
Improved Decision Quality
Increased Output and Productivity
Decreased Decision Making Time
Increased Process(es) and Product Quality
Capture Scarce Expertise
Can Work with Incomplete or Uncertain Information
Enhancement of Problem Solving and Decision Making
Improved Decision Making Processes
Knowledge Transfer to Remote Locations
Enhancement of Other MIS

28. Lead to Improved decision making
Improved products and customer service
Sustainable strategic advantage
May enhance organization’s image

29. Problems and Limitations of Expert Systems
Knowledge is not always readily available
Expertise can be hard to extract from humans
Expert system users have natural cognitive limits
ES work well only in a narrow domain of knowledge
Knowledge engineers are rare and expensive
Lack of trust by end-users
ES may not be able to arrive at valid conclusions
ES sometimes produce incorrect recommendations

30. Expert System Success Factors
Most Critical Factors
Champion in Management
User Involvement and Training
Plus
The level of knowledge must be sufficiently high
There must be (at least) one cooperative expert
The problem must be qualitative (fuzzy) not quantitative
The problem must be sufficiently narrow in scope
The ES shell must be high quality, and naturally store and manipulate the knowledge
A friendly user interface
Important and difficult enough problem

31. For Success
1. Business applications justified by strategic impact (competitive advantage)
2. Well-defined and structured applications

32. Keep Going!

33. Knowledge Acquisition
Knowledge Engineering
Knowledge acquisition, representation, validation, inferencing, explanation and maintenance
Involves the cooperation of human experts
Synergistic effect

34. Knowledge Engineering Process Activities
Knowledge Acquisition
Knowledge Validation
Knowledge Representation
Inferencing
Explanation and Justification

35. Knowledge Engineering Process
validation
(test cases)
Sources of knowledge
(experts, others)
Knowledge
Acquisition
Encoding
Knowledge
base
Knowledge
Representation
Explanation
justification
Inferencing

36. Knowledge Acquisition Methods
Manual (Interviews)
Semiautomatic (Expert-driven)
Automatic (Computer Aided - Induction driven)

37. Interviews Most Common Knowledge Acquisition: Face-to-face interviews
Interview Types
Unstructured (informal)
Semi-structured
Structured

38. Before a knowledge engineer interviews the expert(s)
Recommendation
Before a knowledge engineer interviews the expert(s)
1. Interview a less knowledgeable (minor) expert
Helps the knowledge engineer
Learn about the problem
Learn its significance
Learn about the expert(s)
Learn who the users will be
Understand the basic terminology
Identify readable sources
2. Next read about the problem
3. Then, interview the expert(s) (much more effectively)

39. Induction/Knowledge Table Example
Induction tables (knowledge maps) focus the knowledge acquisition process
Choosing a hospital clinic facility site

41. Knowledge Table Exercise
Choose a restaurant for lunch in Athens

42. Keep Going!

43. Knowledge Representation
Once acquired, knowledge
must be organized for use

44. Introduction
A good knowledge representation naturally represents the problem domain
An unintelligible knowledge representation is wrong
Most artificial intelligence systems consist of
Knowledge Base
Inference Mechanism (Engine)

45. Many knowledge representation schemes
Knowledge Base
Forms the system's intelligence source
Inference mechanism uses to reason and draw conclusions
Inference mechanism: Examines the knowledge base to answer questions, solve problems or make decisions within the domain
Many knowledge representation schemes
Can be programmed and stored in memory
Are designed for use in reasoning
Major knowledge representation schemas:
Production rules
Frames

46. Production Rules Condition-Action Pairs
IF this condition (or premise or antecedent) occurs,
THEN some action (or result, or conclusion, or consequence) will (or should) occur
IF the stop light is red AND you have stopped, THEN a right turn is OK

47. Each production rule in a knowledge base represents an autonomous chunk of expertise
When combined and fed to the inference engine, the set of rules behaves synergistically
Rules can be viewed as a simulation of the cognitive behavior of human experts
Rules represent a model of actual human behavior

48. Forms of Rules IF premise, THEN conclusion
IF your income is high, THEN your chance of being audited by the IRS is high

49. More on Rules Inclusion of ELSE More Complex Rules
IF your income is high, OR your deductions are unusual, THEN your chance of being audited by the IRS is high, OR ELSE your chance of being audited is low
More Complex Rules
IF credit rating is high AND salary is more than $30,000, OR assets are more than $75,000, AND pay history is not "poor," THEN approve a loan up to $10,000, and list the loan in category "B.”
Action part may have more information: THEN "approve the loan" and "refer to an agent"

50. Advantages of Rules Easy to understand (natural form of knowledge)
Easy to derive inference and explanations
Easy to modify and maintain
Easy to combine with uncertainty
Rules are frequently independent

51. Limitations of Rules Complex knowledge requires many rules
Builders like rules (hammer syndrome)
Search limitations in systems with many rules

52. Multiple Knowledge Representations
Rules + Frames
Others
Knowledge Representation Must Support
Acquiring knowledge
Retrieving knowledge
Reasoning

53. Considerations for Evaluating a Knowledge Representation
Naturalness, uniformity and understandability
Degree to which knowledge is explicit (declarative) or embedded in procedural code
Modularity and flexibility of the knowledge base
Efficiency of knowledge retrieval and the heuristic power of the inference procedure

54. Production rules and frames works well in practice
No single knowledge representation method is ideally suited by itself for all tasks
Multiple knowledge representations: each tailored to a different subtask
Production rules and frames works well in practice
Object-oriented knowledge representations
Hypermedia

55. Keep Going!

56. Inference Techniques

57. Reasoning in Artificial Intelligence
Knowledge must be processed (reasoned with)
Computer program accesses knowledge for inferencing
Inference engine or control program
Rule interpreter (in rule-based systems)
Directs search through the knowledge base

58. Inferencing with Rules: Forward and Backward Chaining
Firing a rule: When all of the rule's hypotheses (the “if parts”) are satisfied
Can check every rule in the knowledge base in a forward or backward direction
Continues until no more rules can fire, or until a goal is achieved

59. Forward and Backward Chaining
Chaining: Linking a set of pertinent rules
Search process: directed by a rule interpreter approach:
Forward chaining. If the premise clauses match the situation, then the process attempts to assert the conclusion
Backward chaining. If the current goal is to determine the correct conclusion, then the process attempts to determine whether the premise clauses (facts) match the situation

60. Backward Chaining
Goal-driven - Start from a potential conclusion (hypothesis), then seek evidence that supports (or contradicts) it
Often involves formulating and testing intermediate hypotheses (or subhypotheses)

61. Forward Chaining
Data-driven - Start from available information as it becomes available, then try to draw conclusions
What to use?
If all facts available up front (as in auditing) - forward chaining
Diagnostic problems - backward chaining

62. Representing Uncertainty
Numeric
Graphic
Symbolic

63. Numeric Uncertainty Representation
Scale (0-1, 0-100)
0 = Complete uncertainty
1 or 100 = Complete certainty
Problems with Cognitive Biases
People May be Inconsistent at Different Times

64. Keep Going!

65. Expert Systems Demo
EXSYS (Corvid)

66. Keep Going!

67. Intelligent Systems Development
(Rapid) Prototyping

68. Prototyping: ES Development Life Cycle (PADI)
Nonlinear process
Planning
Analysis
Design
Implementation
Prototype

69. Software Classification: Technology Levels
Expert System Applications (Specific ES)
Shells
Hybrid Systems
Support Tools, Facilities,
and Construction Aids
Programming Languages

70. Rapid Prototyping and a Demonstration Prototype
Build a small prototype
Test, improve, expand
Demonstrate and analyze feasibility
Complete design

71. Rapid Prototyping Crucial to ES development Small-scale system
Includes knowledge representation
Small number of rules
For proof of concept

72. What We’ve Done Basic definitions/methods/ideas
Advanced definitions/methods/ideas
How to KBS/ES
KBS/ES with a business focus
Very rich area – still much potential in business

73. Questions Comments Opinions Coffee?