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CHAPTER 10. Knowledge-Based Decision Support: Artificial Intelligence and Expert 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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chapter 10

CHAPTER 10

Knowledge-Based Decision Support: Artificial Intelligence and Expert Systems

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
Knowledge-Based Decision Support: Artificial Intelligence and Expert Systems
  • Managerial Decision Makers are Knowledge Workers
  • Use Knowledge in Decision Making
  • Accessibility to Knowledge Issue
  • 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

ai concepts and definitions
AI Concepts and Definitions
  • Many Definitions
  • AI Involves Studying Human Thought Processes
  • 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

artificial intelligence
Artificial Intelligence
  • 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)

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

ai objectives
AI Objectives
  • Make machines smarter (primary goal)
  • Understand what intelligence is (Nobel Laureate purpose)
  • 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

signs of intelligence
Signs of Intelligence
  • 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

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
More Signs of Intelligence
  • 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

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
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

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
AI Represents Knowledge as Sets of Symbols

A symbolis a string of characters that stands for some real-world concept

Examples

  • 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

symbol structures relationships
Symbol Structures (Relationships)
  • (DEFECTIVE product)
  • (LEASED-BY product defendant)
  • (EQUAL (LIABILITY defendant) 0.8)
  • 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

slide11
AI Programs Manipulate Symbols to Solve Problems
  • Symbols and Symbol Structures Form Knowledge Representation
  • Artificial Intelligence Dealings Primarily with Symbolic, Nonalgorithmic Problem-Solving Methods

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
Characteristics of Artificial Intelligence
  • Numeric versus Symbolic
  • 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

heuristic methods for processing information
Heuristic Methods for Processing Information
  • Search
  • Inferencing

“AI is the branch of computer science that deals with ways of representing knowledge using symbols rather than numbers and with rules-of-thumb, or heuristic, methods for processing information”.

ai advantages over natural intelligence
AI Advantages Over Natural Intelligence
  • 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

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
Natural Intelligence Advantages over AI
  • Natural intelligence is creative
  • People use sensory experience directly
  • Can use a widecontext of experience in 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

knowledge in artificial intelligence
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

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

Copyright 1998, Prentice Hall, Upper Saddle River, NJ

uses of knowledge
Uses of Knowledge
  • 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)

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

Copyright 1998, Prentice Hall, Upper Saddle River, NJ

knowledge bases
Knowledge Bases
  • Search the Knowledge Base for Relevant Facts and Relationships
  • Reach One or More Alternative Solutions to a Problem
  • 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

how artificial intelligence differs from conventional computing
How Artificial Intelligence Differs from Conventional Computing

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

Copyright 1998, Prentice Hall, Upper Saddle River, NJ

conventional computing
Conventional Computing
  • 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)

table 10 1 how conventional computers process data
Table 10.1: How Conventional Computers Process Data
  • Calculate
  • Perform Logic
  • Store
  • Retrieve
  • Translate
  • Sort
  • Edit
  • Make Structured Decisions
  • Monitor
  • Control

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

Copyright 1998, Prentice Hall, Upper Saddle River, NJ

ai computing
AI Computing
  • 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

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

Copyright 1998, Prentice Hall, Upper Saddle River, NJ

ai computing cont d
AI Computing (cont’d)
  • 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)

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

Copyright 1998, Prentice Hall, Upper Saddle River, NJ

ai computing cont d24
AI Computing (cont’d)
  • Caution: AI is NOT magic
  • 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

table 6 2 artificial intelligence vs conventional programming
Table 6.2: Artificial Intelligence vs. Conventional Programming

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

Copyright 1998, Prentice Hall, Upper Saddle River, NJ

major ai areas
Major AI Areas
  • 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

additional ai areas
Additional AI Areas
  • News Summarization
  • Language Translation
  • Fuzzy Logic
  • Genetic Algorithms
  • 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

an expert system solution
An Expert System Solution

General Electric's (GE) : Top Locomotive Field Service Engineer was Nearing Retirement

Traditional Solution: Apprenticeship but would like

  • A more effective and dependable way to disseminate expertise
  • To prevent valuable knowledge from retiring
  • To minimize extensive travel or moving the locomotives
  • 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

es introduction
(ES) Introduction
  • 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

history of expert systems
History of Expert Systems

1. Early to Mid-1960s

    • 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

slide31
2. Mid-1960s: Special-purpose ES programs
    • DENDRAL
    • MYCIN
  • Researchers recognized that the problem-solving 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

slide32
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
  • Limited Success Because
    • Knowledge is Too Broad and Diverse
    • Efforts to Solve Fairly General Knowledge-Based Problems were Premature

7

slide33
BUT
  • Several knowledge representations worked

Key Insight

  • The power of an ES is derived from the specific knowledge it possesses, not from the particular formalisms and inference schemes it employs

8

4 early 1980s
4. Early 1980s
  • ES Technology Starts to go Commercial
    • XCON
    • XSEL
    • CATS-1
  • Programming Tools and Shells Appear
    • EMYCIN
    • EXPERT
    • META-DENDRAL
    • EURISKO
  • About 1/3 of These Systems Are Very Successful and Are Still in Use

9

latest es developments
Latest ES Developments
  • 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

slide36
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

expert systems
Expert Systems
  • 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

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

expert systems38
Expert Systems
  • Provide Direct Application of Expertise
  • Expert Systems Do Not Replace Experts, But They
    • Make their Knowledge and Experience More Widely Available
    • Permit Nonexperts to Work Better

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

expert systems39
Expert Systems
  • Expertise
  • Transferring Experts
  • Inferencing
  • Rules
  • Explanation Capability

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

expertise
Expertise
  • 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

human expert behaviors
Human Expert Behaviors
  • Recognize and formulate the problem
  • Solve problems quickly and properly
  • Explain the solution
  • Learn from experience
  • Restructure knowledge
  • Break rules
  • Determine relevance
  • Degrade gracefully

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

human experts
Human Experts
  • Knowledge acquisition from human experts

the “paradox of expertise”

17

transferring expertise
Transferring Expertise
  • 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

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
Two Knowledge Types
  • Facts
  • Procedures (usually rules)

Regarding the Problem Domain

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

inferencing
Inferencing
  • Reasoning (Thinking)
  • The computer is programmed so that it can make inferences
  • Performed by the Inference Engine

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

rules
Rules
  • IF-THEN-ELSE
  • Explanation Capability
    • By the justifier, or explanation subsystem
  • 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

knowledge as rules
Knowledge as Rules
  • 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

structure of expert systems
Structure of Expert Systems
  • Development Environment
  • Consultation (Runtime) Environment

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

Inference

Engine

Three Major ES Components

User Interface

Knowledge

Base

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

slide50

ES Shell

Working

Memory

User Interface

Inference

Engine

Knowledge

Base

Explanation

Facility

Knowledge

Acquisition

Database,

Spreadsheets, etc.

Basic ES Structure

26

all es components
All ES Components
  • 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)

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

knowledge base
Knowledge Base
  • The knowledge base contains the knowledge necessary for understanding, formulating, and solving problems
  • 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

inference engine
Inference Engine
  • The brain of the ES
  • The control structure (rule interpreter)
  • Provides methodology for reasoning

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

user interface
User Interface
  • Language processor for friendly, problem-oriented communication
  • NLP, or menus and graphics

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
The Human Element in Expert Systems
  • Builder and User
  • Expert and Knowledge engineer.
  • The Expert
    • Has the special knowledge, judgment, experience and methods to give advice and solve problems
    • Provides knowledge about task performance

35

the knowledge engineer
The Knowledge Engineer
  • 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

the user
The User
  • 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 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

how expert systems work
How Expert Systems Work

Major Activities of

ES Construction and Use

  • Development
  • Consultation
  • Improvement

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

es development
ES Development
  • Construction of the knowledge base
  • Knowledge separated into
    • 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

es shell
ES Shell
  • Includes All Generic ES Components
  • 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 software development packages
Expert Systems Shells Software Development Packages
  • Exsys
  • InstantTea
  • K-Vision
  • KnowledgePro

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
Problem Areas Addressed by Expert Systems
  • Interpretation systems
  • Prediction systems
  • Diagnostic systems
  • Design systems
  • Planning systems
  • Monitoring systems
  • Debugging systems
  • Repair systems
  • Instruction systems
  • Control systems

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
Expert SystemsBenefits
  • 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

lead to
Lead to
  • Improved decision making
  • Improved products and customer service
  • Sustainable strategic advantage
  • May enhance organization’s image

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
Problems and Limitations of Expert Systems
  • 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
  • ES sometimes produce incorrect recommendations

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
Expert System Success Factors
  • 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
    • Important and difficult enough problem

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson

6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

for success
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. Aronson

6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

expert systems types
Expert Systems Types
  • Expert Systems Versus Knowledge-based Systems
  • Rule-based Expert Systems
  • Frame-based Systems
  • Hybrid Systems
  • Model-based Systems
  • Ready-made (Off-the-Shelf) Systems
  • Real-time Expert Systems

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
ES on the Web
  • 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