aiintro-ch10 (1)

7/29/2019 aiIntro-ch10 (1)

1/69

1

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

7/29/2019 aiIntro-ch10 (1)

2/69

2

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



7/29/2019 aiIntro-ch10 (1)

3/69

3

AI Concepts and Definitions

n Many Definitions

n AI Involves Studying Human Thought

Processes

n Representing Thought Processes on

Machines



7/29/2019 aiIntro-ch10 (1)

4/69

4

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)



7/29/2019 aiIntro-ch10 (1)

5/69

5

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])



7/29/2019 aiIntro-ch10 (1)

6/69

6

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



7/29/2019 aiIntro-ch10 (1)

7/69

7

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



7/29/2019 aiIntro-ch10 (1)

8/69

8

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



7/29/2019 aiIntro-ch10 (1)

9/69

9

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


7/29/2019 aiIntro-ch10 (1)

10/69

10

Symbol Structures

(Relationships)n (DEFECTIVE product)

n (LEASED-BY product defendant)n (EQUAL (LIABILITY defendant) 0.8)

n tastes_good (chocolate).



7/29/2019 aiIntro-ch10 (1)

11/69

11

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



7/29/2019 aiIntro-ch10 (1)

12/69

12

Characteristics of

Artificial Intelligencen Numeric versus Symbolic

n Algorithmic versus Nonalgorithmic



7/29/2019 aiIntro-ch10 (1)

13/69

13

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.

7/29/2019 aiIntro-ch10 (1)

14/69

14

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



7/29/2019 aiIntro-ch10 (1)

15/69

15

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



7/29/2019 aiIntro-ch10 (1)

16/69

16

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


Copyright 1998, Prentice Hall, Upper Saddle River, NJ

7/29/2019 aiIntro-ch10 (1)

17/69

17

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)



7/29/2019 aiIntro-ch10 (1)

18/69

18

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)



7/29/2019 aiIntro-ch10 (1)

19/69

19

How Artificial IntelligenceDiffers from Conventional

Computing



7/29/2019 aiIntro-ch10 (1)

20/69

20

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)

7/29/2019 aiIntro-ch10 (1)

21/69

21

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



7/29/2019 aiIntro-ch10 (1)

22/69

22

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



7/29/2019 aiIntro-ch10 (1)

23/69

23

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)



7/29/2019 aiIntro-ch10 (1)

24/69

24

AI Computing (contd)

n Caution: AI is NOT magic

n AI is a unique approach to programming

computers

(Table 6.2)



7/29/2019 aiIntro-ch10 (1)

25/69

25

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



7/29/2019 aiIntro-ch10 (1)

26/69

26

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

7/29/2019 aiIntro-ch10 (1)

27/69

27

Additional AI Areas

n News Summarization

n Language Translation

n Fuzzy Logic

n Genetic Algorithms

n Intelligent Software Agents



7/29/2019 aiIntro-ch10 (1)

28/69

283

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

7/29/2019 aiIntro-ch10 (1)

29/69

294

(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

7/29/2019 aiIntro-ch10 (1)

30/69

305

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

7/29/2019 aiIntro-ch10 (1)

31/69

316

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

7/29/2019 aiIntro-ch10 (1)

32/69

327

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

7/29/2019 aiIntro-ch10 (1)

33/69

338

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

7/29/2019 aiIntro-ch10 (1)

34/69

349

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

7/29/2019 aiIntro-ch10 (1)

35/69

3510

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)

7/29/2019 aiIntro-ch10 (1)

36/69

3611

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.

7/29/2019 aiIntro-ch10 (1)

37/69

37

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



7/29/2019 aiIntro-ch10 (1)

38/69

38

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



7/29/2019 aiIntro-ch10 (1)

39/69

39

Expert Systems

n Expertise

n Transferring Experts

n Inferencing

n Rules

n Explanation Capability



7/29/2019 aiIntro-ch10 (1)

40/69

40

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

7/29/2019 aiIntro-ch10 (1)

41/69

41

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



7/29/2019 aiIntro-ch10 (1)

42/69

4217

Human Experts

n Knowledge acquisition from human

experts

the paradox of expertise

7/29/2019 aiIntro-ch10 (1)

43/69

43

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



7/29/2019 aiIntro-ch10 (1)

44/69

44

Two Knowledge Types

n Facts

n Procedures (usually rules)

Regarding the Problem Domain



7/29/2019 aiIntro-ch10 (1)

45/69

45

Inferencing

n Reasoning (Thinking)

n The computer is programmed so that

it can make inferences

n Performed by the Inference Engine



7/29/2019 aiIntro-ch10 (1)

46/69

46

Rules

n IF-THEN-ELSE

n Explanation Capability

By the justifier, or explanation

subsystem

n ES versus Conventional Systems



7/29/2019 aiIntro-ch10 (1)

47/69

4722

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.

7/29/2019 aiIntro-ch10 (1)

48/69

48

Structure of

Expert Systems

n Development Environment

n Consultation (Runtime) Environment



7/29/2019 aiIntro-ch10 (1)

49/69

49

Three Major ES

Components

User

Interface

Inference

Engine

Knowledge

Base



7/29/2019 aiIntro-ch10 (1)

50/69

5026

Knowledge

Base

Inference

EngineUser Interface

ExplanationFacility

Working

Memory

Knowledge

Acquisition

ES Shell

Database,

Spreadsheets, etc.

Basic ES Structure

S C

7/29/2019 aiIntro-ch10 (1)

51/69

51

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)



7/29/2019 aiIntro-ch10 (1)

52/69

52

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

7/29/2019 aiIntro-ch10 (1)

53/69

53

Inference Engine

n The brain of the ES

n The control structure (rule

interpreter)n Provides methodology for reasoning



7/29/2019 aiIntro-ch10 (1)

54/69

54

User Interface

n Language processorfor friendly,

problem-oriented communication

n NLP, or menus and graphics



7/29/2019 aiIntro-ch10 (1)

55/69

5535

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

7/29/2019 aiIntro-ch10 (1)

56/69

5636

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

7/29/2019 aiIntro-ch10 (1)

57/69

5737

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

7/29/2019 aiIntro-ch10 (1)

58/69

58

How Expert Systems Work

Major Activities of

ES Construction and Use

n Development

n Consultation

n Improvement



7/29/2019 aiIntro-ch10 (1)

59/69

5940

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

7/29/2019 aiIntro-ch10 (1)

60/69

60

ES Shell

n Includes All Generic ES Components

n But No Knowledge

EMYCIN from MYCIN

(E=Empty)



Expert Systems Shells

7/29/2019 aiIntro-ch10 (1)

61/69

61

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

7/29/2019 aiIntro-ch10 (1)

62/69

62

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


7/29/2019 aiIntro-ch10 (1)

63/69

63

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

7/29/2019 aiIntro-ch10 (1)

64/69

64

Lead to

n Improved decision making

n Improved products and customer

servicen Sustainable strategic advantage

n May enhance organizations image


P bl d Li it ti f

7/29/2019 aiIntro-ch10 (1)

65/69

65

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


Expert System

7/29/2019 aiIntro-ch10 (1)

66/69

66

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

7/29/2019 aiIntro-ch10 (1)

67/69

67

For Success

1. Business applications justified by

strategic impact (competitive

advantage)2. Well-defined and structured

applications


7/29/2019 aiIntro-ch10 (1)

68/69

68

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


7/29/2019 aiIntro-ch10 (1)

69/69

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

aiintro-ch10 (1)

Documents