1. Chapter 10 Intelligent Decision Support Systems
Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition
Chapter 10 Intelligent Decision Support Systems
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© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

2. Learning Objectives
Describe the basic concepts and evolution in artificial intelligence.
Understand the importance of knowledge in decision support.
Examine the concepts of rule-based expert systems.
Learn the architecture of rule-based expert systems.
Understand the benefits and limitations of rule based systems for decision support.
Identify proper applications of expert systems.
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3. Intelligent Systems in KPN Telecom and Logitech Vignette
Problems in maintaining computers with varying hardware and software configurations
Rule-based system developed
Captures, manages, automates installation and maintenance
Knowledge-based core
User-friendly interface
Knowledge management module employs natural language processing unit
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4. AI Application
Intelligence: A degree of Learning and reasoning (evidence and conclusion) behavior usually task or problem-solving oriented.
التعلم بالملاحظة، السمع بالمحاضرة بالوعظ وبالممارسة وارقى اشكال التعلم، الاستنتاج باعمال التفكير العميق التأملي
Decision situation can be so complex that Data and Model management alone my not be sufficient & additional support can be provided by Expert Systems to substitute for human expertise in supplying the necessary knowledge.
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

5. Fundamentals of Intelligent systems
AI is a dynamic, varied , growing field.
ES is constructed through Knowledge engineering:
Knowledge Acquisition. (collecting)
Know. Representation. (organize into knowledge base)
Inference (Deduction, Conclusion from evidence)
Intelligent& system development
(Acquisition, Reasoning, Evidence, Conclusion.)
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

6. Knowledge-Based DS “Can be provided by a variety of AI tools, ES being the primary one.”
Managerial Decision Makers are Knowledge Workers and naturally they incorporate knowledge in their DM. In this age, the abundance of knowledge and the enormous numbers of its resources, only a knowledge-base DSS can enhance as a tool the capabilities of D. Makers and Computerized DSS
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

7. Artificial Intelligence Definition Machines that mimic human intelligence.
“The study of human thought process
Duplicate it by machine.”
“AI is behavior by machine that, if performed by human being, would be called Intelligent.”
“AI is a study of how to make computers do things at which, at the moment, people are better.” Rich & Knight ‘91
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

8. Deep Blue & Garry Kasparov
The best chess player ever lived.
The 1st time a computer demonstrated intelligence in an are required human intelligence.
IBM RS/6000 SP Machine capable of:
Examining 200 million moves per second.
50 billion positions in single move per 3 Min.
The victory of the computer does not imply that the computer intelligence will prevail, it dose indicate the potential of AI.
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

9. Abilities as signs of Intelligence
Learning or understanding from experience.
Interpreting, making sense out of ambiguities.
Rapid response to varying situations.
Applying reasoning to problem-solving.
Manipulating environment by applying knowledge.
Thinking and reasoning.
Dealing with Perplexing and puzzling situation.
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10. AI Turing test Alan Turing
“A computer can be considered smart only when a human interviewer conversing with unseen human being and unseen computer can not determine which is which.”
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

11. Artificial Intelligence Characteristics focusing on DM and problem solving
Symbolic processing
Computers process numerically, people think symbolically
Computers follow algorithms
Step by step
Humans are heuristic
Rule of thumb
Gut feelings
Intuitive
Heuristics
Symbols combined with rule of thumb processing.
one doesn’t have to rethink completely what to do every time a similar problem is encountered
Inference
Applies heuristics to infer from facts
Machine learning
Mechanical learning
Inductive learning
Artificial neural networks
Genetic algorithms
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12. 10.3 Development of Artificial Intelligence
Primitive solutions
Development of general purpose methods
Applications targeted at specific domain
Expert systems
Advanced problem-solving
Integration of multiple techniques
Multiple domains
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13. 10.4 Artificial Intelligence Concepts
Expert systems
Human knowledge stored on machine for use in problem-solving
Natural language processing
Allows user to use native language instead of English
Speech recognition
Computer understanding spoken language
Sensory systems
Vision, tactile, and signal processing systems
Robotics
Sensory systems combine with programmable electromechanical device to perform manual labor
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14. Artificial Intelligence Concepts
Vision and scene recognition
Computer intelligence applied to digital information from machine
Neural computing
Mathematical models simulating functional human brain
Intelligent computer-aided instruction
Machines used to tutor humans
Intelligent tutoring systems
Game playing
Investigation of new strategies combined with heuristics
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15. Artificial Intelligence Concepts
Language translation
Programs that translate sentences from one language to another without human interaction
Fuzzy logic
Extends logic from Boolean true/false to allow for partial truths
Imprecise reasoning
Inexact knowledge
Genetic algorithms
Computers simulate natural evolution to identify patterns in sets of data
Intelligent agents
Computer programs that automatically conduct tasks
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16. Experts Expertise 10.5 Experts
Have special knowledge, judgment, and experience
Can apply these to solve problems
Higher performance level than average person
Relative
Faster solutions
Recognize patterns
Expertise
Task specific knowledge of experts
Acquired from reading, training, practice
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17. Expert Systems Features
Expertise
Capable of making expert level decisions
Symbolic reasoning
Knowledge represented symbolically
Reasoning mechanism symbolic
Deep knowledge
Knowledge base contains complex knowledge
Self-knowledge
Able to examine own reasoning
Explain why conclusion reached
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18. Difference s, Human Expert & Expert System Advantages & Short-Comings
Feature
Human Expert
Expert System
Mortality
yes no
Knowledge transfer
Hard
Easy
Knowledge Documentation
Decision Consistency
Low
High
Unit Usage Cost
Creativity
Adaptability
Knowledge Scope
Broad
Narrow
Knowledge Type
Common sense and Technical
Technical
Knowledge Content
Experience
Symbols
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19. 10.6 Applications of Expert Systems
DENDRAL project
Applied knowledge or rule-based reasoning commands
Deduced likely molecular structure of compounds
MYCIN
Rule-based system for diagnosing bacterial infections
XCON
Rule-based system to determine optimal systems configuration
Credit analysis
Ruled-based systems for commercial lenders
Pension fund adviser
Knowledge-based system analyzing impact of regulation and conformance requirements on fund status
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20. Applications Finance Data processing Marketing Human resources
Insurance evaluation, credit analysis, tax planning, financial planning and reporting, performance evaluation
Data processing
Systems planning, equipment maintenance, vendor evaluation, network management
Marketing
Customer-relationship management, market analysis, product planning
Human resources
HR planning, performance evaluation, scheduling, pension management, legal advising
Manufacturing
Production planning, quality management, product design, plant site selection, equipment maintenance and repair
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21. Environments, Ex Sys Structure 1- Consultation (runtime) 2- Development
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22. Major Components of Expert Systems
Knowledge base
Facts
Special heuristics to direct use of knowledge
Inference engine
Brain
Control structure
Rule interpreter
User interface
Language processor
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

23. Additional Components of Expert Systems
Knowledge acquisition subsystem
Accumulates, transfers, and transforms expertise to computer
Workplace
Blackboard
Area of working memory
Decisions
Plan, agenda, solution
Justifier
Explanation subsystem
Traces responsibility for conclusions
Knowledge refinement system
Analyzes knowledge and use for learning and improvements
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24. 10.8 How Ex Sys work 1- Knowledge Presentation
Production rules
IF-THEN rules combine with conditions to produce conclusions
Easy to understand
New rules easily added
Uncertainty
Semantic networks
Logic statements
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

25. 2-Inference Engine Forward chaining Backward chaining
Looks for the IF part of rule first
Selects path based upon meeting all of the IF requirements
Backward chaining
Starts from conclusion and hypothesizes that it is true
Identifies IF conditions and tests their veracity
If they are all true, it accepts conclusion
If they fail, then discards conclusion
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27. 10.9 General Problems Suitable for Expert Systems
Interpretation systems
Surveillance, image analysis, signal interpretation
Prediction systems
Weather forecasting, traffic predictions, demographics
Diagnostic systems
Medical, mechanical, electronic, software diagnosis
Design systems
Circuit layouts, building design, plant layout
Planning systems
Project management, routing, communications, financial plans
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

28. General Problems Suitable for Expert Systems
Monitoring systems
Air traffic control, fiscal management tasks
Debugging systems
Mechanical and software
Repair systems
Incorporate debugging, planning, and execution capabilities
Instruction systems
Identify weaknesses in knowledge and appropriate remedies
Control systems
Life support, artificial environment
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29. © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

30. Benefits of Expert Systems
Increased outputs
Increased productivity
Decreased decision-making time
Increased process and product quality
Reduced downtime
Capture of scarce expertise
Flexibility
Ease of complex equipment operation
Elimination of expensive monitoring equipment
Operation in hazardous environments
Access to knowledge and help desks
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

31. Benefits of Expert Systems
Ability to work with incomplete, imprecise, uncertain data
Provides training
Enhanced problem solving and decision-making
Rapid feedback
Facilitate communications
Reliable decision quality
Ability to solve complex problems
Ease of knowledge transfer to remote locations
Provides intelligent capabilities to other information systems
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32. Limitations Knowledge not always readily available
Difficult to extract expertise from humans
Approaches vary
Natural cognitive limitations
Vocabulary limited
Wrong recommendations
Lack of end-user trust
Knowledge subject to biases
Systems may not be able to arrive at conclusions
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

33. Success Factors Management champion User involvement Training
Expertise from cooperative experts
Qualitative, not quantitative, problem
User-friendly interface
Expert’s level of knowledge must be high
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

34. Types of Expert Systems
Rule-based Systems
Knowledge represented by series of rules
Frame-based Systems
Knowledge represented by frames
Hybrid Systems
Several approaches are combined, usually rules and frames
Model-based Systems
Models simulate structure and functions of systems
Off-the-shelf Systems
Ready made packages for general use
Custom-made Systems
Meet specific need
Real-time Systems
Strict limits set on system response times
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang