CHAPTER 10
Knowledge-Based Decision
Support: Artificial Intelligence and
Expert Systems
1
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Knowledge-Based Decision
Support: Artificial Intelligence
and Expert Systems
n
n
n
n
Managerial Decision Makers are
Knowledge Workers
Use Knowledge in Decision Making
Accessibility to Knowledge Issue
Knowledge-Based Decision
Support: Applied Artificial
Intelligence
2
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
AI Concepts and Definitions
n
n
n
Many Definitions
AI Involves Studying Human Thought
Processes
Representing Thought Processes on
Machines
3
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Artificial Intelligence
n
n
n
Behavior by a machine that, if
performed by a human being, would
be considered intelligent
“…study of how to make computers
do things at which, at the moment,
people are better” (Rich and Knight
[1991])
Theory of how the human mind
works (Mark Fox)
4
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
AI Objectives
n
n
n
Make machines smarter (primary
goal)
Understand what intelligence is
(Nobel Laureate purpose)
Make machines more useful
(entrepreneurial purpose)
(Winston and Prendergast [1984])
5
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Signs of Intelligence
n
n
n
n
Learn or understand from experience
Make sense out of ambiguous or
contradictory messages
Respond quickly and successfully to
new situations
Use reasoning to solve problems
6
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
More Signs of Intelligence
n
n
n
n
n
Deal with perplexing situations
Understand and infer in ordinary,
rational ways
Apply knowledge to manipulate the
environment
Think and reason
Recognize the relative importance of
different elements in a situation
7
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Turing Test for Intelligence
A computer can be considered to be
smart only when a human
interviewer, “conversing” with both
an unseen human being and an
unseen computer, can not determine
which is which
8
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
AI Represents Knowledge
as Sets of Symbols
A symbol is a string of characters that
stands for some real-world concept
Examples
n
n
n
n
Product
Defendant
0.8
Chocolate
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
9
Symbol Structures
(Relationships)
n
n
n
n
(DEFECTIVE product)
(LEASED-BY product defendant)
(EQUAL (LIABILITY defendant) 0.8)
tastes_good (chocolate).
10
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
n
AI Programs Manipulate Symbols to
Solve Problems
n
Symbols and Symbol Structures
Form Knowledge Representation
n
Artificial Intelligence Dealings
Primarily with Symbolic,
Nonalgorithmic Problem-Solving
Methods
11
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Characteristics of
Artificial Intelligence
n
n
Numeric versus Symbolic
Algorithmic versus Nonalgorithmic
12
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Heuristic Methods for
Processing Information
n
n
Search
Inferencing
“AI is the branch of computer science that deals
with ways of representing knowledge using
symbols rather than numbers and with rules-ofthumb, or heuristic, methods for processing
information”.
13
AI Advantages Over Natural
Intelligence
n
n
n
n
n
n
n
More permanent
Ease of duplication and dissemination
Less expensive
Consistent and thorough
Can be documented
Can execute certain tasks much faster
than a human can
Can perform certain tasks better than
many or even most people
14
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Natural Intelligence
Advantages over AI
n
n
n
Natural intelligence is creative
People use sensory experience
directly
Can use a wide context of experience
in different situations
AI - Very Narrow Focus
15
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Knowledge in
Artificial Intelligence
Knowledge encompasses the implicit and
explicit restrictions placed upon objects
(entities), operations, and relationships
along with general and specific heuristics
and inference procedures involved in the
situation being modeled
Of data, information, and knowledge,
KNOWLEDGE is most abstract and in the
smallest quantity
16
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Uses of Knowledge
n
n
n
n
n
Knowledge consists of facts, concepts, theories,
heuristic methods, procedures, and relationships
Knowledge is also information organized and
analyzed for understanding and applicable to
problem solving or decision making
Knowledge base - the collection of knowledge
related to a problem (or opportunity) used in an AI
system
Typically limited in some specific, usually narrow,
subject area or domain
The narrow domain of knowledge, and that an AI
system must involve some qualitative aspects of
decision making (critical for AI application
success)
17
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Knowledge Bases
n
n
n
Search the Knowledge Base for
Relevant Facts and Relationships
Reach One or More Alternative
Solutions to a Problem
Augments the User (Typically a
Novice)
18
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
How Artificial Intelligence
Differs from Conventional
Computing
19
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Conventional Computing
n
n
n
n
n
Based on an Algorithm (clearly defined,
step-by-step procedure)
Mathematical Formula or Sequential
Procedure
Converted into a Computer Program
Uses Data (Numbers, Letters, Words)
Limited to Very Structured, Quantitative
Applications
(Table 10.1)
20
Table 10.1: How Conventional
Computers Process Data
n
n
n
n
n
n
n
n
n
n
Calculate
Perform Logic
Store
Retrieve
Translate
Sort
Edit
Make Structured Decisions
Monitor
Control
21
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
AI Computing
n
n
n
n
Based on symbolic representation and
manipulation
A symbol is a letter, word, or number
represents objects, processes, and their
relationships
Objects can be people, things, ideas,
concepts, events, or statements of fact
Create a symbolic knowledge base
22
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
AI Computing (cont’d)
n
n
n
Uses various processes to manipulate the
symbols to generate advice or a
recommendation
AI reasons or infers with the knowledge
base by search and pattern matching
Hunts for answers
(Algorithms often used in search)
23
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
AI Computing (cont’d)
n
Caution: AI is NOT magic
n
AI is a unique approach to programming
computers
(Table 6.2)
24
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Table 6.2: Artificial Intelligence
vs. Conventional Programming
D im en sio n
A rtificia l In tellig en ce
C o n v en tio n a l P ro g ra m m in g
P ro cessin g
P rim arily S y m b o lic
P rim arily A lg o rith m ic
N a tu re o f In p u t
S ea rch
E x p la n a tio n
M a jo r In terest
S tru ctu re
C a n b e In co m p lete
H eu ristic (M o stly )
P ro v id ed
K n ow led g e
S ep a ra tio n o f C o n tro l
fro m K n ow led g e
N a tu re o f O u tp u t C a n b e In co m p lete
M a in ten a n ce a n d E a sy B eca u se o f
U p d a te
M o d u la rity
H a rd w a re
M a in ly W ork sta tio n s a n d
P erso n a l C o m p u ters
R ea so n in g
L im ited , b u t Im p ro v in g
C a p a b ility
M u st b e C o m p lete
A lg o rith m s
U su a lly N o t P ro v id ed
D a ta , In fo rm a tio n
C o n tro l In teg ra ted w ith
In fo rm a tio n (D a ta )
M u st b e C o rrect
U su a lly D ifficu lt
A ll T y p es
N one
25
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Major AI Areas
n
n
n
n
n
n
n
Expert Systems
Natural Language Processing
Speech Understanding
Robotics and Sensory Systems
Computer Vision and Scene
Recognition
Intelligent Computer-Aided
Instruction
Neural Computing
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
26
Additional AI Areas
n
n
n
n
n
News Summarization
Language Translation
Fuzzy Logic
Genetic Algorithms
Intelligent Software Agents
27
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
An Expert System Solution
General Electric's (GE) : Top Locomotive Field Service
Engineer was Nearing Retirement
Traditional Solution: Apprenticeship but would like
n A more effective and dependable way to disseminate
expertise
n To prevent valuable knowledge from retiring
n To minimize extensive travel or moving the locomotives
n
n
To MODEL the way a human troubleshooter works
– Months of knowledge acquisition
– 3 years of prototyping
A novice engineer or technician can perform at an
expert’s level
– On a personal computer
– Installed at every railroad repair shop served by GE
3
28
(ES) Introduction
n
n
n
Expert System vs. knowledge-based
system
An Expert System is a system that
employs human knowledge captured
in a computer to solve problems that
ordinarily require human expertise
ES imitate the expert’s reasoning
processes to solve specific problems
4
29
History of
Expert Systems
1. Early to Mid-1960s
–
n
n
One attempt: the General-purpose Problem
Solver (GPS)
General-purpose Problem Solver (GPS)
A procedure developed by Newell and
Simon [1973] from their Logic Theory
Machine –
Attempted to create an "intelligent"
computer
•
–
–
general problem-solving methods applicable
across domains
Predecessor to ES
Not successful, but a good start
5
30
2. Mid-1960s: Special-purpose ES programs
–
–
n
DENDRAL
MYCIN
Researchers recognized that the problemsolving mechanism is only a small part of a
complete, intelligent computer system
–
–
–
–
General problem solvers cannot be used to build
high performance ES
Human problem solvers are good only if they
operate in a very narrow domain
Expert systems must be constantly updated with
new information
The complexity of problems requires a
considerable amount of knowledge about the
problem area
6
31
3. Mid 1970s
–
–
–
Several Real Expert Systems Emerge
Recognition of the Central Role of
Knowledge
AI Scientists Develop
•
•
n
Comprehensive knowledge representation
theories
General-purpose, decision-making procedures
and inferences
Limited Success Because
–
–
Knowledge is Too Broad and Diverse
Efforts to Solve Fairly General KnowledgeBased Problems were Premature
7
32
BUT
n
Several knowledge representations
worked
Key Insight
n
The power of an ES is derived from the
specific knowledge it possesses, not from
the particular formalisms and inference
schemes it employs
8
33
4. Early 1980s
n
ES Technology Starts to go Commercial
–
–
–
n
Programming Tools and Shells Appear
–
–
–
–
n
XCON
XSEL
CATS-1
EMYCIN
EXPERT
META-DENDRAL
EURISKO
About 1/3 of These Systems Are Very
Successful and Are Still in Use
9
34
Latest ES Developments
n
n
n
n
n
Many tools to expedite the
construction of ES at a reduced cost
Dissemination of ES in thousands of
organizations
Extensive integration of ES with other
CBIS
Increased use of expert systems in
many tasks
Use of ES technology to expedite IS
construction (ES Shell)
10
35
n
n
n
n
n
n
The object-oriented programming
approach in knowledge representation
Complex systems with multiple
knowledge sources, multiple lines of
reasoning, and fuzzy information
Use of multiple knowledge bases
Improvements in knowledge
acquisition
Larger storage and faster processing
computers
The Internet to disseminate software
and expertise.
11
36
Expert Systems
n
n
n
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 Areas or
Tasks
37
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Expert Systems
n
Provide Direct Application of
Expertise
n
Expert Systems Do Not Replace
Experts, But They
– Make their Knowledge and Experience
More Widely Available
– Permit Nonexperts to Work Better
38
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Expert Systems
n
n
n
n
n
Expertise
Transferring Experts
Inferencing
Rules
Explanation Capability
39
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Expertise
n
n
The extensive, task-specific knowledge
acquired from training, reading and
experience
– Theories about the problem area
– Hard-and-fast rules and procedures
– Rules (heuristics)
– Global strategies
– Meta-knowledge (knowledge about
knowledge)
– Facts
Enables experts to be better and faster than
nonexperts
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
40
Human Expert Behaviors
n
n
n
n
n
n
n
n
Recognize and formulate the problem
Solve problems quickly and properly
Explain the solution
Learn from experience
Restructure knowledge
Break rules
Determine relevance
Degrade gracefully
41
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Human Experts
n
Knowledge acquisition from human
experts
the “paradox of expertise”
17
42
Transferring Expertise
n
n
n
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
43
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Two Knowledge Types
n
n
Facts
Procedures (usually rules)
Regarding the Problem Domain
44
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Inferencing
n
n
n
Reasoning (Thinking)
The computer is programmed so that
it can make inferences
Performed by the Inference Engine
45
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Rules
n
IF-THEN-ELSE
n
Explanation Capability
– By the justifier, or explanation
subsystem
ES versus Conventional Systems
n
46
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Knowledge as Rules
n
MYCIN rule example:
IF the infection is meningitis
AND patient has evidence of serious skin or soft tissue
infection
AND organisms were not seen on the stain of the culture
AND type of infection is bacterial
THEN There is evidence that the organism (other than
those seen on cultures or smears) causin the infection
is Staphylococus coagpus.
22
47
Structure of
Expert Systems
n
n
Development Environment
Consultation (Runtime) Environment
48
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Three Major ES
Components
User
Interface
Inference
Engine
Knowledge
Base
49
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
ES Shell
Working
Memory
User Interface
Inference
Engine
Knowledge
Base
Explanation
Facility
Database,
Spreadsheets, etc.
Knowledge
Acquisition
Basic ES Structure
26
50
All ES Components
n
n
n
n
n
n
n
n
n
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
(See Figure 10.3)
51
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Knowledge Base
n
The knowledge base contains the knowledge
necessary for understanding, formulating, and
solving problems
n
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
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
52
Inference Engine
n
n
n
The brain of the ES
The control structure (rule
interpreter)
Provides methodology for reasoning
53
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
User Interface
n
n
Language processor for friendly,
problem-oriented communication
NLP, or menus and graphics
54
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
The Human Element in
Expert Systems
n
Builder and User
Expert and Knowledge engineer.
n
The Expert
n
–
–
Has the special knowledge, judgment,
experience and methods to give advice and
solve problems
Provides knowledge about task
performance
35
55
The Knowledge Engineer
n
n
Helps the expert(s) structure the
problem area by interpreting and
integrating human answers to
questions, drawing analogies, posing
counterexamples, and bringing to light
conceptual difficulties
Usually also the System Builder
36
56
The User
n
Possible Classes of Users
–
–
–
–
n
A non-expert client seeking direct advice the ES acts as a Consultant or Advisor
A student who wants to learn - an
Instructor
An ES builder improving or increasing the
knowledge base - a Partner
An expert - a Colleague or Assistant
The Expert and the Knowledge
Engineer Should Anticipate Users'
Needs and Limitations When
Designing ES
37
57
How Expert Systems Work
Major Activities of
ES Construction and Use
n
n
n
Development
Consultation
Improvement
58
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
ES Development
n
n
Construction of the knowledge base
Knowledge separated into
–
–
n
n
Declarative (factual) knowledge and
Procedural knowledge
Construction (or acquisition) of an
inference engine, a blackboard, an
explanation facility, and any other
software
Determine appropriate knowledge
representations
40
59
ES Shell
n
n
Includes All Generic ES Components
But No Knowledge
– EMYCIN from MYCIN
– (E=Empty)
60
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Expert Systems Shells
Software Development
Packages
n
n
n
n
Exsys
InstantTea
K-Vision
KnowledgePro
61
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Problem Areas Addressed
by Expert Systems
n
n
n
n
n
n
n
n
n
n
Interpretation systems
Prediction systems
Diagnostic systems
Design systems
Planning systems
Monitoring systems
Debugging systems
Repair systems
Instruction systems
Control systems
62
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Expert Systems Benefits
n
n
n
n
n
n
n
n
n
n
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
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
63
Lead to
n
Improved decision making
Improved products and customer
service
Sustainable strategic advantage
n
May enhance organization’s image
n
n
64
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Problems and Limitations of
Expert Systems
n
n
n
n
n
n
n
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
65
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Expert System
Success Factors
n
n
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
66
Decision
Support
Systems
and
Intelligent
Systems,
Efraim
Turban
and
Jay
E.
Aronson
– Important
and difficult enough problem
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
For Success
1. Business applications justified by
strategic impact (competitive
advantage)
2. Well-defined and structured
applications
67
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Expert Systems Types
n
n
n
n
n
n
n
Expert Systems Versus Knowledgebased Systems
Rule-based Expert Systems
Frame-based Systems
Hybrid Systems
Model-based Systems
Ready-made (Off-the-Shelf) Systems
Real-time Expert Systems
68
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
ES on the Web
n
n
n
n
n
Provide knowledge and advice
Help desks
Knowledge acquisition
Spread of multimedia-based expert
systems (Intelimedia systems)
Support ES and other AI
technologies provided to the
Internet/Intranet
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
69
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Artificial Intelligence and Expert Systems