aiintro-ch10 (1)
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CHAPTER 10
Knowledge-Based Decision
Support: Artificial Intelligence andExpert Systems
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Knowledge-Based Decision
Support: Artificial Intelligence
and Expert Systems
n Managerial Decision Makers areKnowledge Workers
n Use Knowledge in Decision Making
n Accessibility to Knowledge Issue
n Knowledge-Based Decision
Support: Applied Artificial
Intelligence
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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AI Concepts and Definitions
n Many Definitions
n AI Involves Studying Human Thought
Processes
n Representing Thought Processes on
Machines
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Artificial Intelligencen Behaviorby a machine that, if
performed by a human being, would
be considered intelligent
n study of how to make computers
do things at which, at the moment,people are better (Rich and Knight
[1991])
n Theory of how the human mindworks (Mark Fox)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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AI Objectives
n Make machines smarter (primarygoal)
n Understand what intelligence is(Nobel Laureate purpose)
n Make machines more useful
(entrepreneurial purpose)
(Winston and Prendergast [1984])
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Signs of Intelligence
n Learn orunderstand from experience
n Make sense out of ambiguous or
contradictory messages
n Respond quickly and successfully to
new situations
n Use reasoning to solve problems
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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More Signs of Intelligence
n Deal with perplexing situations
n Understand and infer in ordinary,rational ways
n Apply knowledge to manipulate theenvironment
n Think and reason
n Recognize the relative importance of
different elements in a situation
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Turing Test for Intelligence
A computer can be considered to be
smart only when a human
interviewer, conversing with bothan unseen human being and an
unseen computer, can not determine
which is which
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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AI Represents Knowledge
as Sets of Symbols
A symbolis a string of characters thatstands for some real-world concept
Examples
n Product
n Defendant
n 0.8
n ChocolateDecision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Symbol Structures
(Relationships)n (DEFECTIVE product)
n (LEASED-BY product defendant)n (EQUAL (LIABILITY defendant) 0.8)
n tastes_good (chocolate).
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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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-SolvingMethods
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Characteristics of
Artificial Intelligencen Numeric versus Symbolic
n Algorithmic versus Nonalgorithmic
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Heuristic Methods for
Processing Information
n Search
n Inferencing
AI is the branch of computer science that deals
with ways of representing knowledge usingsymbols rather than numbers and with rules-of-
thumb, or heuristic, methods for processing
information.
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AI Advantages Over Natural
Intelligencen More permanentn Ease of duplication and dissemination
n
Less expensiven Consistent and thorough
n Can be documented
n Can execute certain tasks much fasterthan a human can
n Can perform certain tasks better thanmany or even most people
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Natural Intelligence
Advantages over AIn Natural intelligence is creative
n People use sensory experience
directly
n Can use a widecontext of experiencein different situations
AI - Very Narrow Focus
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Knowledge in
Artificial Intelligence
Knowledge encompasses the implicit andexplicit restrictions placed upon objects(entities), operations, and relationshipsalong with general and specific heuristicsand inference procedures involved in thesituation being modeled
Of data, information, and knowledge,KNOWLEDGE is most abstract and in thesmallest quantity
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
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Uses of Knowledge
n Knowledge consists of facts, concepts, theories,heuristic methods, procedures, and relationships
n Knowledge is also information organized andanalyzed for understanding and applicable toproblem solving or decision making
n Knowledge base - the collection of knowledgerelated to a problem (or opportunity) used in anAI system
n Typically limited in some specific, usually narrow,
subject area ordomainn The narrow domain of knowledge, and that an AI
system must involve some qualitative aspects ofdecision making (critical for AI applicationsuccess)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
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Knowledge Bases
n Search the Knowledge Base forRelevant Facts and Relationships
n Reach One or More AlternativeSolutions to a Problem
n Augments the User (Typically a
Novice)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
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How Artificial IntelligenceDiffers from Conventional
Computing
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
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Conventional Computing
n Based on an Algorithm(clearly defined,step-by-step procedure)
n Mathematical Formula or SequentialProcedure
n Converted into a Computer Programn Uses Data (Numbers, Letters, Words)
n Limited to Very Structured, QuantitativeApplications
(Table 10.1)
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Table 10.1: How Conventional
Computers Process Datan Calculaten Perform Logic
n Store
n Retrieve
n Translate
n Sort
n Edit
n Make Structured Decisions
n Monitor
n Control
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
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AI Computing
n Based on symbolic representation andmanipulation
n A symbol is a letter, word, or number
represents objects, processes, and theirrelationships
n Objects can be people, things, ideas,concepts, events, or statements of fact
n Create a symbolic knowledge base
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
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AI Computing (contd)
n Uses various processes to manipulate the
symbols to generate advice or a
recommendationn AI reasons or infers with the knowledge
base by search and pattern matching
n Hunts for answers
(Algorithms often used in search)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
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AI Computing (contd)
n Caution: AI is NOT magic
n AI is a unique approach to programming
computers
(Table 6.2)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
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Table 6.2: Artificial Intelligence
vs. Conventional ProgrammingDimension Artificial Intelligence Conventional ProgrammingProcessing Primarily Symbolic Primarily Algorithmic
Nature of Input Can be Incomplete Must be Complete
Search Heuristic (Mostly) Algorithms
Explanation Provided Usually Not Provided
Major Interest Knowledge Data, InformationStructure Separation of Control
from Knowledge
Control Integrated with
Information (Data)
Nature of Output Can be Incomplete Must be Correct
Maintenance and
Update
Easy Because of
Modularity
Usually Difficult
Hardware Mainly Workstations andPersonal Computers
All Types
Reasoning
Capability
Limited, but Improving None
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
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Major AI Areas
n Expert Systems
n Natural Language Processing
n Speech Understanding
n Robotics and Sensory Systems
n Computer Vision and Scene
Recognitionn Intelligent Computer-Aided
Instruction
n Neural ComputingDecision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Additional AI Areas
n News Summarization
n Language Translation
n Fuzzy Logic
n Genetic Algorithms
n Intelligent Software Agents
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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An Expert System Solution
General Electric's (GE) : Top Locomotive Field Service Engineer wasNearing 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 To MODEL the way a human troubleshooter works
Months of knowledge acquisition
3 years of prototyping
n A novice engineer or technician can perform at an experts level On a personal computer
Installed at every railroad repair shop served by GE
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(ES) Introduction
n Expert Systemvs. knowledge-based system
n An Expert Systemis a system that employs
human knowledge captured in a computer tosolve problems that ordinarily require human
expertise
n ES imitatethe experts reasoning processes to
solve specific problems
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History of
Expert Systems1. Early to Mid-1960s
One attempt: the General-purpose Problem Solver(GPS)
n General-purpose Problem Solver (GPS)
n A procedure developed by Newell and Simon[1973] from their Logic Theory Machine -
Attempted to create an "intelligent" computer
general problem-solving methods applicable acrossdomains
Predecessor to ES
Not successful, but a good start
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2. Mid-1960s: Special-purpose ES programs DENDRAL
MYCINn Researchers recognized that the problem-solving
mechanism is only a small part of a complete,intelligent computer system
General problem solvers cannotbe 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 updatedwith newinformation
The complexity of problems requires a considerable amountofknowledge about the problem area
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3. Mid 1970s Several Real Expert Systems Emerge
Recognition of the Central Role of Knowledge
AI Scientists Develop
Comprehensive knowledge representation theories
General-purpose, decision-making procedures and
inferences
n Limited Success Because
Knowledge is Too Broad and Diverse
Efforts to Solve Fairly General Knowledge-Based
Problems were Premature
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BUT
n Several knowledge representationsworked
Key Insight
n The power of an ES is derived from the specificknowledge it possesses, not from the particularformalisms and inference schemes it employs
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4. Early 1980s
n ES Technology Starts to go Commercial
XCON
XSEL
CATS-1
n Programming Tools and Shells Appear EMYCIN
EXPERT
META-DENDRAL
EURISKO
n About 1/3 of These Systems Are Very Successful andAre Still in Use
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Latest ES Developments
n Many tools to expedite the construction of ESat a reduced cost
n Dissemination of ES in thousands oforganizations
n Extensive integration of ES with other CBIS
n Increased use of expert systems in many tasks
n Use of ES technology to expedite ISconstruction (ES Shell)
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n The object-oriented programming approach inknowledge representation
n Complex systems with multiple knowledgesources, multiple lines of reasoning, and fuzzyinformation
n Use of multiple knowledge bases
n Improvements in knowledge acquisition
n Larger storage and faster processingcomputers
n The Internet to disseminate software andexpertise.
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Expert Systems
n Attempt to Imitate Expert Reasoning
Processes and Knowledge in Solving
Specific Problemsn Most Popular Applied AI Technology
Enhance Productivity
Augment Work Forces
n Narrow Problem-Solving Areas orTasks
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Expert Systems
n Provide Direct Application of
Expertise
n Expert Systems Do Not Replace
Experts, But They
Make their Knowledge and ExperienceMore Widely Available
Permit Nonexperts to Work Better
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
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Expert Systems
n Expertise
n Transferring Experts
n Inferencing
n Rules
n Explanation Capability
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
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Expertise
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
n Enables experts to be better and faster than
nonexpertsDecision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Human Expert Behaviors
n Recognize and formulate the problem
n Solve problems quickly and properly
n Explain the solutionn Learn from experience
n Restructure knowledge
n Break rulesn Determine relevance
n Degrade gracefully
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
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Human Experts
n Knowledge acquisition from human
experts
the paradox of expertise
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Transferring Expertise
n Objective of an expert system
To transfer expertise from an expert to a
computer system and
Then on to other humans (nonexperts)
n Activities
Knowledge acquisition
Knowledge representation
Knowledge inferencing
Knowledge transfer to the user
n Knowledge is stored in a knowledge base
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
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Two Knowledge Types
n Facts
n Procedures (usually rules)
Regarding the Problem Domain
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
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Inferencing
n Reasoning (Thinking)
n The computer is programmed so that
it can make inferences
n Performed by the Inference Engine
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
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Rules
n IF-THEN-ELSE
n Explanation Capability
By the justifier, or explanation
subsystem
n ES versus Conventional Systems
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Knowledge as Rules
n MYCIN rule example:IF the infection is meningitis
AND patient has evidence of serious skin or soft tissue
infectionAND 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 infectionis Staphylococus coagpus.
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Structure of
Expert Systems
n Development Environment
n Consultation (Runtime) Environment
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
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Three Major ES
Components
User
Interface
Inference
Engine
Knowledge
Base
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Knowledge
Base
Inference
EngineUser Interface
ExplanationFacility
Working
Memory
Knowledge
Acquisition
ES Shell
Database,
Spreadsheets, etc.
Basic ES Structure
S C
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All ES Componentsn Knowledge Acquisition Subsystem
n Knowledge Basen Inference Engine
n User Interface
n Blackboard (Workplace)
n Explanation Subsystem (Justifier)
n Knowledge Refining System
n User
n Most ES do not have a Knowledge RefinementComponent
(See Figure 10.3)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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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 representationDecision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Inference Engine
n The brain of the ES
n The control structure (rule
interpreter)n Provides methodology for reasoning
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
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User Interface
n Language processorfor friendly,
problem-oriented communication
n NLP, or menus and graphics
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
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The Human Element in
Expert Systems
n Builder and User
n Expert and Knowledge engineer.
n The Expert
Has the special knowledge, judgment, experience
and methods to give advice and solve problems
Provides knowledge about task performance
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The Knowledge Engineer
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
n Usually also the System Builder
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The User
n Possible Classes of Users 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 theknowledge base - a Partner
An expert - a Colleagueor Assistant
n The Expert and the Knowledge Engineer
Should Anticipate Users' Needs andLimitations When Designing ES
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How Expert Systems Work
Major Activities of
ES Construction and Use
n Development
n Consultation
n Improvement
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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ES Development
n Construction of the knowledge base
n Knowledge separated into
Declarative(factual) knowledge and
Procedural knowledge
n Construction (or acquisition) of an inferenceengine, a blackboard, an explanation facility,and any other software
n Determine appropriate knowledgerepresentations
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ES Shell
n Includes All Generic ES Components
n But No Knowledge
EMYCIN from MYCIN
(E=Empty)
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
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Expert Systems Shells
Software Development
Packagesn Exsys
n InstantTea
n K-Vision
n KnowledgePro
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Problem Areas Addressed
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Problem Areas Addressed
by Expert Systemsn Interpretation systems
n Prediction systems
n Diagnostic systems
n Design systemsn Planning systems
n Monitoring systems
n Debugging systems
n Repair systems
n Instruction systems
n Control systems
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Expert SystemsBenefits
n Improved Decision Qualityn Increased Output and Productivity
n Decreased Decision Making Time
n Increased Process(es) and Product Quality
n Capture Scarce Expertise
n Can Work with Incomplete or Uncertain
Information
n Enhancement of Problem Solving and Decision
Making
n Improved Decision Making Processes
n Knowledge Transfer to Remote Locations
n Enhancement of Other MISDecision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Lead to
n Improved decision making
n Improved products and customer
servicen Sustainable strategic advantage
n May enhance organizations image
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
P bl d Li it ti f
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Problems and Limitations of
Expert Systems
n Knowledge is not always readily available
n Expertise can be hard to extract from humans
n Expert system users have natural cognitivelimits
n ES work well only in a narrow domain ofknowledge
n Knowledge engineers are rare and expensiven Lack of trust by end-users
n ES may not be able to arrive at valid
conclusionsDecision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Expert System
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Expert System
Success Factorsn Most Critical Factors
Champion in Management
User Involvement and Training
n 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
Important and difficult enough problemDecision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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For Success
1. Business applications justified by
strategic impact (competitive
advantage)2. Well-defined and structured
applications
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Expert Systems Types
n Expert Systems Versus Knowledge-
based Systems
n Rule-based Expert Systemsn Frame-based Systems
n Hybrid Systems
n Model-based Systemsn Ready-made (Off-the-Shelf) Systems
n Real-time Expert Systems
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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ES on the Web
n Provide knowledge and advice
n Help desks
n Knowledge acquisition
n Spread of multimedia-based expert
systems (Intelimedia systems)
n Support ES and other AI
technologies provided to the
Internet/Intranet
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