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

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Knowledge Acquisition, Representation, and Reasoning

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

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.

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

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

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

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

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

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

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

11-6

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

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Knowledge

• Sources – Documented

• Written, viewed, sensory, behavior

– Undocumented• Memory

– Acquired from• Human senses• Machines

•

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

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Knowledge

• Categories– Declarative

• Descriptive representation

– Procedural • How things work under different

circumstances• How to use declarative knowledge

– Problem solving

– Metaknowledge• Knowledge about knowledge

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

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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

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

11-10

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

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

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

11-12

Manual Methods

• Interviews– Structured

• Goal-oriented• Walk through

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

– Semistructured

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

11-13

Manual Methods

• Process tracking– Track reasoning processes

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

• Observation– Motor movements– Eye movements

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

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

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

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

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

11-16

Automatic Methods

• Data mining by computers

• Inductive learning from existing recognized cases

• Neural computing mimicking human brain

• Genetic algorithms using natural selection

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

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

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

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

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

11-19

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

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

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

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

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

11-22

Semantic Networks

• Graphical depictions

• Nodes and links • Hierarchical

relationships between concepts

• Reflects inheritance

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

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

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

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

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

11-25

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

11-26

Uncertainty

• Widespread• Important component• Representation

– Numeric scale• 1 to 100

– Graphical presentation• Bars, pie charts

– Symbolic scales• Very likely to very unlikely

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

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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

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

11-28

Expert System Development

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

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

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

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

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

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

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Prototyping

• Rapid production

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

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

11-32

System Development

• Development strategies formalized

• Knowledge base developed

• Interfaces created

• System evaluated and improved

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

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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

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

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