Chapter 10 Intelligent Decision Support Systems

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

10
-
1

Chapter 10

Intelligent Decision Support Systems

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


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

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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.

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

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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.

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.)

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

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

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

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“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


Deep Blue & Garry Kasparov


The best chess player ever lived.


The 1
st

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.


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

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

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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.”

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

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

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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


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



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



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



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

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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


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

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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Environments, Ex Sys Structure

1
-

Consultation (runtime)

2
-

Development

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

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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

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

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Additional Components of Expert
Systems

Additional components

1.
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


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


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

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

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

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

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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

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

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



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

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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


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

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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