© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 10-1 Chapter 10 Intelligent Decision Support.

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

10-1

Chapter 10Intelligent Decision Support

Systems

Turban, Aronson, and Liang Decision Support Systems and Intelligent

Systems, Seventh Edition


10-2

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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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

AI Application

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.© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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Intelligence: A degree of Learning and reasoning (evidence and conclusion) behavior usually task or problem-solving oriented.

وارقى وبالممارسة بالوعظ بالمحاضرة السمع بالمالحظة، التعلمالتأملي العميق التفكير باعمال االستنتاج التعلم، اشكال

Fundamentals of Intelligent systems

• AI is a dynamic, varied , growing field.

• ES is constructed through Knowledge engineering:

1.Knowledge Acquisition. (collecting)

2.Know. Representation. (organize into knowledge base)

3. Inference (Deduction, Conclusion from evidence)

4. Intelligent& system development(Acquisition, Reasoning, Evidence, Conclusion.)


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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


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“The study of human thought processDuplicate 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

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:

1. Examining 200 million moves per second.

2. 50 billion positions in single move per 3 Min.


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The victory of the computer does not imply that the computer intelligence will prevail, it dose indicate the potential of AI.

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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“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.”


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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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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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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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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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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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10.5 Experts

• 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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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

Difference s, Human Expert & Expert System Advantages & Short-

Comings

Feature Human Expert Expert System

Mortality yes no

Knowledge transfer Hard Easy

Knowledge Documentation Hard Easy

Decision Consistency Low High

Unit Usage Cost High Low

Creativity High Low

Adaptability High Low

Knowledge Scope Broad Narrow

Knowledge Type Common sense and Technical

Technical

Knowledge Content Experience Symbols


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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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Applications

• Finance– 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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Environments, Ex Sys Structure1- Consultation (runtime) 2- Development


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Major Components of Expert Systems

Major components– Knowledge base

• Facts • Special heuristics to direct use of knowledge

– Inference engine• Brain• Control structure• Rule interpreter

– User interface• Language processor


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Additional Components of Expert Systems

Additional components1. Knowledge acquisition subsystem

• Accumulates, transfers, and transforms expertise to computer

2. Workplace• Blackboard• Area of working memory • Decisions

– Plan, agenda, solution

3. Justifier• Explanation subsystem

– Traces responsibility for conclusions

4. Knowledge refinement system• Analyzes knowledge and use for learning and

improvements


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10.8 How Ex Sys work1- 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


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2-Inference Engine

• Forward 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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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


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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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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


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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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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


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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


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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

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 10-1 Chapter 10 Intelligent Decision Support.

Documents

liang decision support

edition slide

prentice hall

knowledge acquisition

knowledge base

managerial decision

knowledge workers

abundance of knowledge