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Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition Chapter 11 Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition Chapter 11 Knowledge Acquisition, Representation, and Reasoning © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 1

Learning Objectives • Understand the nature of knowledge. • Learn the knowledge engineering processes. 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, 7 th Edition, Turban, Aronson, and Liang 2

Development of a Real-Time Knowledge-Based System at Eli Lilly Vignette • Problems with fermentation Development of a Real-Time Knowledge-Based System at Eli Lilly Vignette • Problems with fermentation process – Quality parameters difficult to control – Many different employees doing same task – High turnover • 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, 7 th Edition, Turban, Aronson, and Liang 3

Knowledge Engineering • Process of acquiring knowledge from experts and building knowledge base – 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, 7 th Edition, Turban, Aronson, and Liang 4

Knowledge Engineering Process • Acquisition of knowledge – General knowledge or metaknowledge – From 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, 7 th Edition, Turban, Aronson, and Liang 5

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 6

Knowledge • Sources – Documented • Written, viewed, sensory, behavior – Undocumented • Memory Knowledge • Sources – Documented • Written, viewed, sensory, behavior – Undocumented • Memory – Acquired from • Human senses • Machines • © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 7

Knowledge • Levels – Shallow • Surface level • Input-output – Deep • Problem Knowledge • Levels – Shallow • Surface level • Input-output – Deep • Problem solving • Difficult to collect, validate • Interactions betwixt system components © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 8

Knowledge • Categories – Declarative • Descriptive representation – Procedural • How things work 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, 7 th Edition, Turban, Aronson, and Liang 9

Knowledge Engineers • Professionals who elicit knowledge from experts – Empathetic, patient – Broad 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, 7 th Edition, Turban, Aronson, and Liang 10

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 11

Elicitation Methods • Manual – Based on interview – Track reasoning process – Observation 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, 7 th Edition, Turban, Aronson, and Liang 12

Manual Methods • Interviews – Structured • Goal-oriented • Walk through – Unstructured • 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, 7 th Edition, Turban, Aronson, and Liang 13

Manual Methods • Process tracking – Track reasoning processes • Protocol analysis – Document 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, 7 th Edition, Turban, Aronson, and Liang 14

Manual Methods • • • Case analysis Critical incident User discussions Expert commentary Graphs 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, 7 th Edition, Turban, Aronson, and Liang 15

Semiautomatic Methods • Repertory grid analysis – Personal construct theory • Organized, perceptual model 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, 7 th Edition, Turban, Aronson, and Liang 16

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 17

Semiautomatic Methods, continued • Computer based tools features: – Ability to add knowledge to Semiautomatic Methods, continued • Computer based tools features: – Ability to add knowledge to base – Ability to assess, refine knowledge – Visual modeling for construction of domain – Creation of decision trees and rules – Ability to analyze information flows – Integration tools © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 18

Automatic Methods • Data mining by computers • Inductive learning from existing recognized cases 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, 7 th Edition, Turban, Aronson, and Liang 19

Multiple Experts • Scenarios – Experts contribute individually – Primary expert’s information reviewed by Multiple Experts • Scenarios – Experts contribute individually – Primary expert’s information reviewed by secondary experts – Small group decision – Panels for verification and validation • Approaches – – Consensus methods Analytic approaches Automation of process through software usage Decomposition © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 20

Automated Knowledge Acquisition • Induction – Activities • Training set with known outcomes • Automated Knowledge Acquisition • Induction – Activities • Training set with known outcomes • Creates rules for examples • Assesses new cases – Advantages • Limited application • Builder can be expert – Saves time, money © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 21

Automated Knowledge Acquisition – Difficulties • Rules may be difficult to understand • Experts 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 must be cleansed • Limited to certainties • Examples may be insufficient © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 22

Automated Knowledge Acquisition • Interactive induction – Incrementally induced knowledge • General models – Automated Knowledge Acquisition • Interactive induction – Incrementally induced knowledge • General models – Object Network – Based on interaction with expert • interviews – Computer supported • Induction tables • IF-THEN-ELSE rules © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 23

Evaluation, Validation, Verification • Dynamic activities – Evaluation • Assess system’s overall value – 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, 7 th Edition, Turban, Aronson, and Liang 24

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 25

Production Rules • IF-THEN • Independent part, combined with other pieces, to produce better 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 conclusion 1 (OR) ELSE conclusion 2 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 26

Artificial Intelligence Rules • Types – Knowledge rules • Declares facts and relationships • 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, 7 th Edition, Turban, Aronson, and Liang 27

Artificial Intelligence Rules • Advantages – – – Easy to understand, modify, maintain Explanations Artificial Intelligence Rules • Advantages – – – Easy to understand, modify, maintain Explanations are easy to get. Rules are independent. Modification and maintenance are relatively easy. Uncertainty is easily combined with rules. • Limitations – Huge numbers may be required – Designers may force knowledge into rule-based entities – Systems may have search limitations; difficulties in evaluation © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 28

Semantic Networks • Graphical depictions • Nodes and links • Hierarchical relationships between concepts Semantic Networks • Graphical depictions • Nodes and links • Hierarchical relationships between concepts • Reflects inheritance © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 29

Frames • All knowledge about object • Hierarchical structure allows for inheritance • Allows 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, 7 th Edition, Turban, Aronson, and Liang 30

Knowledge Relationship Representations • Decision tables – Spreadsheet format – All possible attributes compared 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, 7 th Edition, Turban, Aronson, and Liang 31

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 32

Reasoning Programs • Inference Engine – Algorithms – Directs search of knowledge base • Reasoning Programs • Inference Engine – Algorithms – Directs search of knowledge base • Forward chaining – Data driven – Start with information, draw conclusions • Backward chaining – Goal driven – Start with expectations, seek supporting evidence – Inference/goal tree • Schematic view of inference process – AND/OR/NOT nodes – Answers why and how • Rule interpreter © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 33

Explanation Facility • Justifier – – – Makes system more understandable Exposes shortcomings Explains Explanation Facility • Justifier – – – Makes system more understandable Exposes shortcomings Explains situations that the user did not anticipate Satisfies user’s psychological and social needs Clarifies underlying assumptions Conducts sensitivity analysis • Types – Why – How – Journalism based • Who, what, where, when, why, how • Why not © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 34

Generating Explanations • Static explanation – Preinsertion of text • Dynamic explanation – Reconstruction Generating Explanations • Static explanation – Preinsertion of text • Dynamic explanation – Reconstruction by rule evaluation • Tracing records or line of reasoning • Justification based on empirical associations • Strategic use of metaknowledge © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 35

Uncertainty • Widespread • Important component • Representation – Numeric scale • 1 to 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, 7 th Edition, Turban, Aronson, and Liang 36

Uncertainty • Probability Ratio – Degree of confidence in conclusion – Chance of occurrence 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, 7 th Edition, Turban, Aronson, and Liang 37

Certainty • Certainty factors – Belief in event based on evidence – Belief and Certainty • Certainty factors – Belief in event based on evidence – Belief and disbelief independent and not combinable – Certainty factors may be combined into one rule – Rules may be combined © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 38

Expert System Development • Phases – Project initialization – Systems analysis and design – Expert System Development • Phases – Project initialization – Systems analysis and design – Prototyping – System development – Implementation – Postimplementation © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 39

Project Initialization • • • Identify problems Determine functional requirements Evaluate solutions Verify and 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, 7 th Edition, Turban, Aronson, and Liang 40

Systems Analysis and Design • Create conceptual system design • Determine development strategy – 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, 7 th Edition, Turban, Aronson, and Liang 41

Prototyping • Rapid production • Demonstration prototype – Small system or part of system 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, 7 th Edition, Turban, Aronson, and Liang 42

System Development • • Development strategies formalized Knowledge base developed Interfaces created System evaluated System Development • • Development strategies formalized Knowledge base developed Interfaces created System evaluated and improved © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 43

Implementation • Adoption strategies formulated • System installed • All parts of system must 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, 7 th Edition, Turban, Aronson, and Liang 44

Postimplementation • Operation of system • Maintenance plans – Review, revision of rules – Postimplementation • Operation of system • Maintenance plans – Review, revision of rules – Data integrity checks – Linking to databases • Upgrading and expansion • Periodic evaluation and testing © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 45

Internet • Facilitates knowledge acquisition and distribution • Problems with use of informal knowledge Internet • Facilitates knowledge acquisition and distribution • Problems with use of informal knowledge • Open knowledge source © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 46