Materi Kuliah Intelligent Decision Support Systems

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Transcript Materi Kuliah Ekonomi

Turban, Aronson, and Liang
Decision Support Systems and Intelligent Systems,
Seventh Edition

Chapter 10
Intelligent Decision Support Systems

www.ekonomi.dikampus.com © 2005 Prentice Hall, Decision Support Systems and 10-1
Intelligent Systems, 7th Edition, Turban, Aronson, and Liang
Learning Objectives


Describe the basic concepts 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, 7th Edition, Turban, Aronson, and Liang
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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Intelligent Systems, 7th Edition, Turban, Aronson, and Liang
Artificial Intelligence


Duplication of human thought process
by machine

Learning from experience

Interpreting ambiguities

Rapid response to varying situations

Applying reasoning to problem-solving

Manipulating environment by applying
knowledge

Thinking and reasoning
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Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Artificial Intelligence
Characteristics


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

Inference

Applies heuristics to infer from facts

Machine learning

Mechanical learning

Inductive learning

Artificial neural networks

Genetic algorithms
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Intelligent Systems, 7th Edition, Turban, Aronson, and Liang
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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Intelligent Systems, 7th Edition, Turban, Aronson, and Liang
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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Intelligent Systems, 7th Edition, Turban, Aronson, and Liang
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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Intelligent Systems, 7th Edition, Turban, Aronson, and Liang
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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Intelligent Systems, 7th Edition, Turban, Aronson, and Liang
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
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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


Consultation (runtime)

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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Intelligent Systems, 7th Edition, Turban, Aronson, and Liang
Additional Components of Expert
Systems


Additional components

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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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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Intelligent Systems, 7th Edition, Turban, Aronson, and Liang
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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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
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Intelligent Systems, 7th Edition, Turban, Aronson, and Liang