Managing Uncertainty in Expert Systems

Jerzy W. Grzymala-Busse

ISBN 10: 0792391691 / ISBN 13: 9780792391692

Editore: Springer US, Springer US Jul 1991, 1991

Riguardo questo articolo

Neuware -3. Textbook for a course in expert systems,if an emphasis is placed on Chapters 1 to 3 and on a selection of material from Chapters 4 to 7. There is also the option of using an additional commercially available sheU for a programming project. In assigning a programming project, the instructor may use any part of a great variety of books covering many subjects, such as car repair. Instructions for mostofthe 'weekend mechanic' books are close stylisticaUy to expert system rules. Contents Chapter 1 gives an introduction to the subject matter; it briefly presents basic concepts, history, and some perspectives ofexpert systems. Then itpresents the architecture of an expert system and explains the stages of building an expert system. The concept of uncertainty in expert systems and the necessity of deal ing with the phenomenon are then presented. The chapter ends with the descrip tion of taxonomy ofexpert systems. Chapter 2 focuses on knowledge representation. Four basic ways to repre sent knowledge in expert systems are presented: first-order logic, production sys tems, semantic nets, and frames. Chapter 3 contains material about knowledge acquisition. Among machine learning techniques, a methodofrule learning from examples is explained in de tail. Then problems ofrule-base verification are discussed. In particular, both consistency and completeness oftherule base are presented.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 246 pp. Englisch.

Codice articolo 9780792391692

Dati bibliografici

• Titolo: Managing Uncertainty in Expert Systems
• Editore: Springer US, Springer US Jul 1991
• Data di pubblicazione: 1991
• Lingua: Inglese
• ISBN 10: 0792391691
• ISBN 13: 9780792391692
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 241 L x 160 A x 18 P (millimetri) 241 millimetri di larghezza per 160 millimetri di altezza per 18 millimetri di profondità
• Peso: 538 grammi

Descrizione articolo

Riassunto

3. Textbook for a course in expert systems,if an emphasis is placed on Chapters 1 to 3 and on a selection of material from Chapters 4 to 7. There is also the option of using an additional commercially available sheU for a programming project. In assigning a programming project, the instructor may use any part of a great variety of books covering many subjects, such as car repair. Instructions for mostofthe "weekend mechanic" books are close stylisticaUy to expert system rules. Contents Chapter 1 gives an introduction to the subject matter; it briefly presents basic concepts, history, and some perspectives ofexpert systems. Then itpresents the architecture of an expert system and explains the stages of building an expert system. The concept of uncertainty in expert systems and the necessity of deal­ ing with the phenomenon are then presented. The chapter ends with the descrip­ tion of taxonomy ofexpert systems. Chapter 2 focuses on knowledge representation. Four basic ways to repre­ sent knowledge in expert systems are presented: first-order logic, production sys­ tems, semantic nets, and frames. Chapter 3 contains material about knowledge acquisition. Among machine learning techniques, a methodofrule learning from examples is explained in de­ tail. Then problems ofrule-base verification are discussed. In particular, both consistency and completeness oftherule base are presented.

Contenuti

1 Introdution.- 1.1 Architecture of an Expert System.- 1.2 Building Expert Systems.- 1.2.1 Construction Stages.- 1.2.2 Expert System Tools.- 1.3 Uncertainty in Expert Systems.- 1.3.1 Sources of Uncertainty.- 1.3.2 Inference under Uncertainty.- 1.4 Taxonomy of Expert Systems.- 1.4.1 By Task Type.- 1.4.2 By User Interaction and Solution Status.- 2 Knowledge Representation.- 2.1 First-Order Logic.- 2.1.1 Propositional Calculus.- 2.1.2 Predicate Calculus.- 2.2 Production Systems.- 2.2.1 Production Rules.- 2.2.2 Data Base.- 2.2.3 Rule Interpreter.- 2.2.3.1 Pattern Matching.- 2.2.3.2 Conflict Resolution.- 2.2.4 Forward Chaining.- 2.2.5 Depth-First and Breadth-First Search.- 2.2.6 Backward Chaining.- 2.2.7 Metarules.- 2.2.8 Forward and Backward Reasoning Versus Chaining.- 2.2.9 Advantages and Disadvantagesof Production Systems.- 2.3 Semantic Nets.- 2.3.1 Basic Properties.- 2.3.2 Extended Semantic Nets.- 2.3.3 Concluding Remarks.- 2.4 Frames.- 2.4.1 Basic Concepts.- 2.4.2 Inference.- 2.4.3 Advantages and Disadvantages of Frame Systems.- Exercises.- 3 Knowledge Acquisition.- 3.1 Manual and Interactive Techniques.- 3.1.1 Interviewing.- 3.1.2 Observation.- 3.1.3 Multiple Experts.- 3.1.4 Psychology-Based Techniques.- 3.1.5 Knowledge Acquisition under Uncertainty.- 3.2 Machine Learning.- 3.3 Rule Learning from Examples.- 3.3.1 Decision Tables.- 3.3.1.1 A Single Decision Table.- 3.3.1.2 A Set of Decision Tables.- 3.3.2 Indiscernibility Relations and Partitions.- 3.3.2.1 Indiscernibility Relations.- 3.3.2.2 Partitions.- 3.3.3 Attribute Dependency and Rule Induction.- 3.3.3.1 Attribute Dependency Inequality.- 3.3.3.2 Equivalent Attribute Sets.- 3.3.3.3 Coverings.- 3.3.4 Checking Attribute Dependency.- 3.3.5 An Algorithm for Finding the Setof all Coverings.- 3.3.6 An Algorithm for Finding a Covering.- 3.3.7 Rule Induction from a Decision Table.- 3.3.7.1 Essential Attributes.- 3.3.7.2 Finding Rules from Coverings.- 3.3.8 Attribute Dependency and Data Bases.- 3.4 Rule Base Verification.- 3.4.1 Consistency.- 3.4.1.1 Redundancy.- 3.4.1.2 Conflict.- 3.4.1.3 Subsumption.- 3.4.1.4 Unnecessary Conditions.- 3.4.1.5 Circularity.- 3.4.2 Completeness.- 3.4.2.1 Unreferenced Attribute Values.- 3.4.2.2 Illegal Attribute and Decision Values.- 3.4.2.3 Unreachable Conditions.- 3.4.2.4 Unreachable Actions.- 3.4.2.5 Unreachable Goals.- 3.4.3 Concluding Remarks.- Exercises.- 4 One-Valued Quantitative Approaches.- 4.1 Probability Theory.- 4.1.1 Definition of a Probability.- 4.1.2 Kolmogorov’s Axioms.- 4.1.3 Conditional Probability.- 4.1.4 Independent Events.- 4.1.5 Bayes’ Rule.- 4.2 Systems using Bayes’ Rule.- 4.2.1 Inference Network.- 4.2.2 Bayesian Updating.- 4.2.3 Uncertain Evidence.- 4.2.4 Multiple Evidences and Single Hypothesis.- 4.2.5 Multiple Evidences and Multiple Hypotheses.- 4.3 Belief Networks.- 4.3.1 Detection of Independences.- 4.3.2 Knowledge Updating.- 4.4 Certainty Factors.- 4.4.1 Basic Concepts.- 4.4.2 Propagation.- 4.4.2.1 Rules with Certain Single Conditions.- 4.4.2.2 Rules with Uncertain Single Conditions.- 4.4.2.3 Multiple Conditions.- 4.5 Concluding Remarks.- Exercises.- 5 Two-Valued Quantitative Approaches.- 5.1 Dempster-Shafer Theory.- 5.1.1 Frame of Discernment.- 5.1.2 Basic Probability Numbers.- 5.1.3 Belief Functions.- 5.1.4 Focal Elements and Core.- 5.1.5 Degrees of Doubt and Plausibility.- 5.1.6 Bayesian Belief Functions.- 5.1.7 Dempster’s Rule of Combination.- 5.1.8 Support Functions.- 5.1.9 Combining Simple Support Functions.- 5.2 Inferno.- 5.2.1 Representation of Uncertainty.- 5.2.2 Relations.- 5.2.3 Propagation.- 5.2.4 Constraints.- 5.2.5 Termination of Propagation.- 5.2.6 Consistency of Information.- 5.3 Concluding Remarks.- Exercises.- 6 Set-Valued Quantitative Approaches.- 6.1 Fuzzy Set Theory.- 6.1.1 Fuzzy Sets and Operations.- 6.1.2 Extended Venn Diagrams.- 6.1.3 Fuzzy Relations and Operations.- 6.1.4 Possibility Theory and Possibility Distributions.- 6.1.5 Linguistic Variables, Linguistic Modifiers, and the Translation Modifier Rule.- 6.1.6 Fuzzy Logic—a PRUF Approach.- 6.1.6.1 Propositions of Fuzzy Logic.- 6.1.6.2 Translation Rules.- 6.1.6.3 Semantic Entailment.- 6.1.6.4 Rules of Inference.- 6.2 Incidence Calculus.- 6.2.1 Incidences.- 6.2.2 Probability Calculus Enhanced by a Correlation.- 6.2.3 Inference.- 6.3 Rough Set Theory.- 6.3.1 Rough Sets.- 6.3.2 Rough Definability of a Set.- 6.3.3 Rough Measures of a Set.- 6.3.4 Partitions.- 6.3.5 Certain and Possible Rules.- 6.4 Concluding Remarks.- Exercises.- 7 Qualitative Approaches.- 7.1 Modal Logics.- 7.2 Nonmonotonicity.- 7.2.1 Nonmonotonic and Autoepistemic Logics.- 7.2.2 Default Logic.- 7.2.3 Circumscription.- 7.2.4 Truth Maintenance System.- 7.2.4.1 Justifications.- 7.2.4.2 Node Types.- 7.2.4.3 Circular Arguments.- 7.2.4.4 Truth Maintenance.- 7.2.4.5 Default Assumptions.- 7.2.5 Reasoned Assumptions.- 7.3 Plausible Reasoning.- 7.4 Heuristic Methods.- 7.4.1 Endorsements.- 7.4.2 CORE.- 7.5 Concluding Remarks.- Exercises.- References.