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Rule-Based Reasoning


Classifier systems and genetic algorithms

Classics

ABSTRACT Classifier systems are massively parallel, message-passing, rule-based systems that learn through credit assignment (the bucket brigade algorithm) and rule discovery (the genetic algorithm). They typically operate in environments that exhibit one or more of the following characteristics: (1) perpetually novel events accompanied by large amounts of noisy or irrelevant data; (2) continual, often real-time, requirements for action; (3) implicitly or inexactly defined goals; and (4) sparse payoff or reinforcement obtainable only through long action sequences. Classifier systems are designed to absorb new information continuously from such environments, devising sets of compet- ing hypotheses (expressed as rules) without disturbing significantly capabilities already acquired. This paper reviews the definition, theory, and extant applications of classifier systems, comparing them with other machine learning techniques, and closing with a discussion of advantages, problems, and possible extensions of classifier systems. Artificial Intelligence, 40 (1-3), 235-82.


Why a Diagram is (sometimes) Worth Ten Thousand Words

Classics

When two representations are informationally equivalent, their computational efficiency depends on the information-processing operators that act on them. Two sets of operators may differ in their capabilities for recognizing patterns, in the inferences they can carry out directly, and in their control strategies (in par- ticular, the control of search). Cognitive Science 11, 65-99




Probabilistic interpretation for MYCIN's certainty factors

Classics

The certainty-factor (CF) model is a commonly used method for managing uncertainty in rule-based systems. We review the history and mechanics of the CF model, and delineate precisely its theoretical and practical limitations. In addition, we examine the belief network, a representation that is similar to the CF model but that is grounded firmly in probability theory. We show that the belief-network representation overcomes many of the limitations of the CF model, and provides a promising approach to the practical construction of expert systems.


Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project

Classics

Artificial intelligence, or AI, is largely an experimental science—at least as much progress has been made by building and analyzing programs as by examining theoretical questions. MYCIN is one of several well-known programs that embody some intelligence and provide data on the extent to which intelligent behavior can be programmed. As with other AI programs, its development was slow and not always in a forward direction. But we feel we learned some useful lessons in the course of nearly a decade of work on MYCIN and related programs. In this book we share the results of many experiments performed in that time, and we try to paint a coherent picture of the work. The book is intended to be a critical analysis of several pieces of related research, performed by a large number of scientists. We believe that the whole field of AI will benefit from such attempts to take a detailed retrospective look at experiments, for in this way the scientific foundations of the field will gradually be defined. It is for all these reasons that we have prepared this analysis of the MYCIN experiments.ContentsContributorsForewordAllen NewellPrefacePart One: BackgroundChapter 1—The Context of the MYCIN ExperimentsChapter 2—The Origin of Rule-Based Systems in AIRandall Davis and Jonathan J. KingPart Two: Using RulesChapter 3—The Evolution of MYCIN’s Rule FormChapter 4—The Structure of the MYCIN SystemWilliam van MelleChapter 5—Details of the Consultation SystemEdward H. ShortliffeChapter 6—Details of the Revised Therapy AlgorithmWilliam J. ClanceyPart Three: Building a Knowledge BaseChapter 7—Knowledge EngineeringChapter 8—Completeness and Consistency in a Rule-Based SystemMotoi Suwa, A. Carlisle Scott, and Edward H. ShortliffeChapter 9—Interactive Transfer of ExpertiseRandall Davis[#p4]] Part Four: Reasoning Under UncertaintyChapter 10—Uncertainty and Evidential SupportChapter 11—A Model of Inexact Reasoning in MedicineEdward H. Shortliffe and Bruce G. BuchananChapter 12—Probabilistic Reasoning and Certainty FactorsJ. Barclay AdamsChapter 13—The Dempster-Shafer Theory of EvidenceJean Gordon and Edward H. ShortliffePart Five: Generalizing MYCINChapter 14—Use of the MYCIN Inference EngineChapter 15—EMYCIN: A Knowledge Engineer’s Tool for Constructing Rule-Based Expert SystemsWilliam van Melle, Edward H. Shortliffe, and Bruce G. BuchananChapter 16—Experience Using EMYCINJames S. Bennett and Robert S. EngelmorePart Six: Explaining the ReasoningChapter 17—Explanation as a Topic of AI ResearchChapter 18—Methods for Generating ExplanationsA. Carlisle Scott, William J. Clancey, Randall Davis, and Edward H. ShortliffeChapter 19—Specialized Explanations for Dosage SelectionSharon Wraith Bennett and A. Carlisle ScottChapter 20—Customized Explanations Using Causal KnowledgeJerold W. Wallis and Edward H. ShortliffePart Seven: Using Other RepresentationsChapter 21—Other Representation FrameworksChapter 22—Extensions to the Rule-Based Formalism for a Monitoring TaskLawrence M. Fagan, John C. Kunz, Edward A. Feigenbaum, and John J. OsbornChapter 23—A Representation Scheme Using Both Frames and RulesJanice S. AikinsChapter 24—Another Look at FramesDavid E. Smith and Jan E. ClaytonPart Eight: TutoringChapter 25—Intelligent Computer-Aided InstructionChapter 26—Use of MYCIN’s Rules for TutoringWilliam J. ClanceyPart Nine: Augmenting the RulesChapter 27—Additional Knowledge StructuresChapter 28—Meta-Level KnowledgeRandall Davis and Bruce G. BuchananChapter 29—Extensions to Rules for Explanation and TutoringWilliam J. ClanceyPart Ten: Evaluating PerformanceChapter 30—The Problem of EvaluationChapter 31—An Evaluation of MYCIN’s AdviceVictor L. Yu, Lawrence M. Fagan, Sharon Wraith Bennett, William J . Clancey, A. Carlisle Scott, John F. Hannigan, Robert L. Blum, Bruce G. Buchanan, and Stanley N. CohenPart Eleven: Designing for Human UseChapter 32—Human Engineering of Medical Expert SystemsChapter 33—Strategies for Understanding Structured EnglishAlain BonnetChapter 34—An Analysis of Physicians’ AttitudesRandy L. Teach and Edward H. ShortliffeChapter 35—An Expert System for Oncology Protocol ManagementEdward H. Shortliffe, A. Carlisle Scott, Miriam B. Bischoff, A. Bruce Campbell, William van MeUe, and Charlotte D. JacobsPart Twelve: ConclusionsChapter 36—Major Lessons from This WorkEpilogAppendixReferencesName IndexSubject IndexReading, MA: Addison-Wesley Publishing Co., Inc.


Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project

Classics

Artificial intelligence, or AI, is largely an experimental science—at least as much progress has been made by building and analyzing programs as by examining theoretical questions. MYCIN is one of several well-known programs that embody some intelligence and provide data on the extent to which intelligent behavior can be programmed. As with other AI programs, its development was slow and not always in a forward direction. But we feel we learned some useful lessons in the course of nearly a decade of work on MYCIN and related programs. In this book we share the results of many experiments performed in that time, and we try to paint a coherent picture of the work. The book is intended to be a critical analysis of several pieces of related research, performed by a large number of scientists. We believe that the whole field of AI will benefit from such attempts to take a detailed retrospective look at experiments, for in this way the scientific foundations of the field will gradually be defined. It is for all these reasons that we have prepared this analysis of the MYCIN experiments.

The complete book in a single file.


Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project

Classics

Artificial intelligence, or AI, is largely an experimental science—at least as much progress has been made by building and analyzing programs as by examining theoretical questions. MYCIN is one of several well-known programs that embody some intelligence and provide data on the extent to which intelligent behavior can be programmed. As with other AI programs, its development was slow and not always in a forward direction. But we feel we learned some useful lessons in the course of nearly a decade of work on MYCIN and related programs. In this book we share the results of many experiments performed in that time, and we try to paint a coherent picture of the work. The book is intended to be a critical analysis of several pieces of related research, performed by a large number of scientists. We believe that the whole field of AI will benefit from such attempts to take a detailed retrospective look at experiments, for in this way the scientific foundations of the field will gradually be defined. It is for all these reasons that we have prepared this analysis of the MYCIN experiments.ContentsContributorsForewordAllen NewellPrefacePart One: BackgroundChapter 1—The Context of the MYCIN ExperimentsChapter 2—The Origin of Rule-Based Systems in AIRandall Davis and Jonathan J. KingPart Two: Using RulesChapter 3—The Evolution of MYCIN’s Rule FormChapter 4—The Structure of the MYCIN SystemWilliam van MelleChapter 5—Details of the Consultation SystemEdward H. ShortliffeChapter 6—Details of the Revised Therapy AlgorithmWilliam J. ClanceyPart Three: Building a Knowledge BaseChapter 7—Knowledge EngineeringChapter 8—Completeness and Consistency in a Rule-Based SystemMotoi Suwa, A. Carlisle Scott, and Edward H. ShortliffeChapter 9—Interactive Transfer of ExpertiseRandall Davis[#p4]] Part Four: Reasoning Under UncertaintyChapter 10—Uncertainty and Evidential SupportChapter 11—A Model of Inexact Reasoning in MedicineEdward H. Shortliffe and Bruce G. BuchananChapter 12—Probabilistic Reasoning and Certainty FactorsJ. Barclay AdamsChapter 13—The Dempster-Shafer Theory of EvidenceJean Gordon and Edward H. ShortliffePart Five: Generalizing MYCINChapter 14—Use of the MYCIN Inference EngineChapter 15—EMYCIN: A Knowledge Engineer’s Tool for Constructing Rule-Based Expert SystemsWilliam van Melle, Edward H. Shortliffe, and Bruce G. BuchananChapter 16—Experience Using EMYCINJames S. Bennett and Robert S. EngelmorePart Six: Explaining the ReasoningChapter 17—Explanation as a Topic of AI ResearchChapter 18—Methods for Generating ExplanationsA. Carlisle Scott, William J. Clancey, Randall Davis, and Edward H. ShortliffeChapter 19—Specialized Explanations for Dosage SelectionSharon Wraith Bennett and A. Carlisle ScottChapter 20—Customized Explanations Using Causal KnowledgeJerold W. Wallis and Edward H. ShortliffePart Seven: Using Other RepresentationsChapter 21—Other Representation FrameworksChapter 22—Extensions to the Rule-Based Formalism for a Monitoring TaskLawrence M. Fagan, John C. Kunz, Edward A. Feigenbaum, and John J. OsbornChapter 23—A Representation Scheme Using Both Frames and RulesJanice S. AikinsChapter 24—Another Look at FramesDavid E. Smith and Jan E. ClaytonPart Eight: TutoringChapter 25—Intelligent Computer-Aided InstructionChapter 26—Use of MYCIN’s Rules for TutoringWilliam J. ClanceyPart Nine: Augmenting the RulesChapter 27—Additional Knowledge StructuresChapter 28—Meta-Level KnowledgeRandall Davis and Bruce G. BuchananChapter 29—Extensions to Rules for Explanation and TutoringWilliam J. ClanceyPart Ten: Evaluating PerformanceChapter 30—The Problem of EvaluationChapter 31—An Evaluation of MYCIN’s AdviceVictor L. Yu, Lawrence M. Fagan, Sharon Wraith Bennett, William J . Clancey, A. Carlisle Scott, John F. Hannigan, Robert L. Blum, Bruce G. Buchanan, and Stanley N. CohenPart Eleven: Designing for Human UseChapter 32—Human Engineering of Medical Expert SystemsChapter 33—Strategies for Understanding Structured EnglishAlain BonnetChapter 34—An Analysis of Physicians’ AttitudesRandy L. Teach and Edward H. ShortliffeChapter 35—An Expert System for Oncology Protocol ManagementEdward H. Shortliffe, A. Carlisle Scott, Miriam B. Bischoff, A. Bruce Campbell, William van MeUe, and Charlotte D. JacobsPart Twelve: ConclusionsChapter 36—Major Lessons from This WorkEpilogAppendixReferencesName IndexSubject IndexReading, MA: Addison-Wesley Publishing Co., Inc.


The epistemology of a rule-based expert system - A framework for explanation

Classics

Production rules are a popular representation for encoding heuristic knowledge in programs for scientific and medical problem solving. However, experience with one of these programs, mycin, indicates that the representation has serious limitations: people other than the original rule authors find it difficult to modify the rule set, and the rules are unsuitable for use in other settings, such as for application to teaching. These problems are rooted in fundamental limitations in mycin's original rule representation: the view that expert knowledge can be encoded as a uniform, weakly structured set of if/then associations is found to be wanting. To illustrate these problems, this paper examines mycin's rules from the perspective of a teacher trying to justify them and to convey a problem-solving approach. We discover that individual rules play different roles, have different kinds of justifications, and are constructed using different rationales for the ordering and choice of premise clauses.