Expert systems

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

11-1

Knowledge Acquisition, Representation, and Reasoning


11-2

Learning Objectives

• Understand the nature of knowledge.• Learn the knowledge engineering processes.• Evaluate different approaches for knowledge

acquisition.• Examine the pros and cons of different

approaches.• Illustrate methods for knowledge verification and

validation.• Examine inference strategies.• Understand certainty and uncertainty processing.


11-3

Development of a Real-Time Knowledge-Based System

• Expert system used to capture knowledge– Expertise available 24 hours a day

• Knowledge engineers developed system by:– Knowledge elicitation

• Interviewing experts and creating knowledge bases

– Knowledge fusion• Fusing individual knowledge bases

– Coding knowledge base– Testing and evaluation of system


11-4

Knowledge Engineering

• Process of acquiring knowledge from experts and building knowledge base– Narrow perspective

• Knowledge acquisition, representation, validation, inference, maintenance

– Broad perspective• Process of developing and maintaining

intelligent system


11-5

Knowledge Engineering Process

• Acquisition of knowledge– General knowledge or metaknowledge– From experts, books, documents, sensors, files

• Knowledge representation– Organized knowledge

• Knowledge validation and verification• Inferences

– Software designed to pass statistical sample data to generalizations

• Explanation and justification capabilities


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11-7

Knowledge

• Sources – Documented

• Written, viewed, sensory, behavior

– Undocumented• Memory

– Acquired from• Human senses• Machines

•


11-8

Knowledge

• Categories– Declarative

• Descriptive representation

– Procedural • How things work under different

circumstances• How to use declarative knowledge

– Problem solving

– Metaknowledge• Knowledge about knowledge


11-9

Knowledge Engineers

• Professionals who elicit knowledge from experts– Empathetic, patient– Broad range of understanding, capabilities

• Integrate knowledge from various sources– Creates and edits code– Operates tools

• Build knowledge base– Validates information– Trains users


11-10


11-11

Elicitation Methods

• Manual– Based on interview– Track reasoning process– Observation

• Semiautomatic– Build base with minimal help from knowledge

engineer– Allows execution of routine tasks with minimal

expert input• Automatic

– Minimal input from both expert and knowledge engineer


11-12

Manual Methods

• Interviews– Structured

• Goal-oriented• Walk through

– Unstructured• Complex domains• Data unrelated and difficult to integrate

– Semistructured


11-13

Manual Methods

• Process tracking– Track reasoning processes

• Protocol analysis– Document expert’s decision-making – Think aloud process

• Observation– Motor movements– Eye movements


11-14

Manual Methods

• Case analysis• Critical incident• User discussions• Expert commentary• Graphs and conceptual models• Brainstorming• Prototyping• Multidimensional scaling for distance matrix• Clustering of elements• Iterative performance review


11-15

Semiautomatic Methods

• Repertory grid analysis– Personal construct theory

• Organized, perceptual model of expert’s knowledge• Expert identifies domain objects and their attributes• Expert determines characteristics and opposites for

each attribute• Expert distinguishes between objects, creating a grid

• Expert transfer system– Computer program that elicits information from

experts– Rapid prototyping– Used to determine sufficiency of available

knowledge


11-16

Automatic Methods

• Data mining by computers

• Inductive learning from existing recognized cases

• Neural computing mimicking human brain

• Genetic algorithms using natural selection


11-17

Automated Knowledge Acquisition

– Difficulties• Rules may be difficult to understand• Experts needed to select attributes• Algorithm-based search process produces

fewer questions• Rule-based classification problems• Allows few attributes• Many examples needed• Examples may be insufficient


11-18

Evaluation, Validation, Verification

• Dynamic activities– Evaluation

• Assess system’s overall value

– Validation• Compares system’s performance to expert’s• Concordance and differences

– Verification• Building and implementing system correctly• Can be automated


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11-20

Production Rules

• IF-THEN• Independent part, combined with

other pieces, to produce better result• Model of human behavior• Examples

– IF condition, THEN conclusion– Conclusion, IF condition– If condition, THEN conclusion1 (OR)

ELSE conclusion2


11-21

Artificial Intelligence Rules

• Types– Knowledge rules

• Declares facts and relationships• Stored in knowledge base

– Inference• Given facts, advises how to proceed• Part of inference engines• Metarules


11-22

Semantic Networks

• Graphical depictions

• Nodes and links • Hierarchical

relationships between concepts

• Reflects inheritance


11-23

Frames

• All knowledge about object• Hierarchical structure allows for inheritance• Allows for diagnosis of knowledge

independence• Object-oriented programming

– Knowledge organized by characteristics and attributes

• Slots• Subslots/facets

– Parents are general attributes– Instantiated to children

• Often combined with production rules


11-24

Knowledge Relationship Representations

• Decision tables– Spreadsheet format– All possible attributes compared to conclusions

• Decision trees– Nodes and links– Knowledge diagramming

• Computational logic– Propositional

• True/false statement– Predicate logic

• Variable functions applied to components of statements


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11-26

Uncertainty

• Widespread• Important component• Representation

– Numeric scale• 1 to 100

– Graphical presentation• Bars, pie charts

– Symbolic scales• Very likely to very unlikely


11-27

Uncertainty

• Probability Ratio– Degree of confidence in conclusion– Chance of occurrence of event

• Bayes Theory– Subjective probability for propositions

• Imprecise• Combines values

• Dempster-Shafer– Belief functions– Creates boundaries for assignments of

probabilities• Assumes statistical independence


11-28

Expert System Development

• Phases– Project initialization– Systems analysis and design– Prototyping– System development– Implementation– Postimplementation


11-29

Project Initialization

• Identify problems• Determine functional requirements• Evaluate solutions• Verify and justify requirements• Conduct feasibility study and cost-benefit

analysis• Determine management issues • Select team• Project approval


11-30

Systems Analysis and Design

• Create conceptual system design• Determine development strategy

– In house, outsource, mixed• Determine knowledge sources• Obtain cooperation of experts• Select development environment

– Expert system shells– Programming languages– Hybrids with tools

• General or domain specific shells• Domain specific tools


11-31

Prototyping

• Rapid production

• Demonstration prototype– Small system or part of system– Iterative– Each iteration tested by users– Additional rules applied to later iterations


11-32

System Development

• Development strategies formalized

• Knowledge base developed

• Interfaces created

• System evaluated and improved


11-33

Implementation

• Adoption strategies formulated

• System installed

• All parts of system must be fully documented and security mechanisms employed

• Field testing if it stands alone; otherwise, must be integrated

• User approval


11-34

Postimplementation

• Operation of system

• Maintenance plans– Review, revision of rules– Data integrity checks– Linking to databases

• Upgrading and expansion

• Periodic evaluation and testing

Expert systems

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