A survey of early work exploring how AI can be used in medicine, with somewhat more technical expositions than in the complementary volume Artificial Intelligence in Medicine."Each chapter is preceded by a brief introduction that outlines our view of its contribution to the field, the reason it was selected for inclusion in this volume, an overview of its content, and a discussion of how the work evolved after the article appeared and how it relates to other chapters in the book.

JANICE S. AIKINS Dr. Aikins received her Ph.D. in computer science from Stanford University in 1980. She is currently a research computer scientist at IBM's Palo Alto Scientific Center. She specializes in designing systems with an emphasis on the explicit representation of control knowledge in expert systems. ROBERT L. BLUM Dr. Blum received his M.D. from the University of California Medical School at San Francisco in 1973. From 1973 to 1976 he did an internship and residency in the Department of Internal Medicine at the Kaiser Foundation Hospital in Oakland, California, where he was chief resident in 1976.

A. YONEZAWA and C. HEWITT 41 REPRESENTATIONS FOR ABSTRACT REASONING 4 A maximal method for set variables in automatic theorem-proving W. W. BLEDSOE

Researchers in the development of medical expert systems have Increasingly recognized the Importance of explanation capabilities in encouraging the acceptance of their programs. One survey of potential users of medical advice systems has suggested that explanation may be the single most important capability of an acceptable clinical decision tool (16). Good explanations serve four functions in a consultation system: 111 they provide a method for examining the program's reasoning if errors arise when the system is being built; 121 they assure users that the reasoning is logical, thereby increasing user acceptance of the system; 131 they may persuade users that unexpected advice is appropriate; and 141 they can educate users in areas where their knowledge may be weak.

We describe an oncology protocol management system, named ONCOCIN, that is designed to assist physicians in the treatment of cancer patients. The system is a set of related programs, one of which is a rule-based reasoner that encompasses the necessary knowledge of cancer chemotherapy. Representation and control techniques are dizcussed, and ONCOCIN is contra7ted with:qstems that could be built using EMYCIN. Of particular Interest is the need to provide ONCOCIN with an interface that will make the system acceptable to oncologists.

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.

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 Heuristic Programming Project of the Stanford University Computer Science Department is a laboratory of about fifty people whose main goals are to model the nature of scientific reasoning processes in various types of scientific problems and various areas of science and medicine; and to construct expert systems — programs that achieve high levels of performance on tasks that normally require significant human expertise for their solution.