Founded in 1972 to develop and disseminate new ideas in computer science, the Information Sciences Institute (ISI) is an off-campus research center of the University of Southern California, with a combined research and support staff of over one hundred. The Institute engages in a broad set of research and application-oriented projects in the computer sciences. These projects range from basic efforts, through development of prototype systems, to operation of a major Arpanet computer facility. The Institute AI research focuses on program synthesis user interfaces, programming environments, natural language, and expert systems. AI researchers are supported by ten personal Lisp workstations, several VAXs, two TOPS-20 systems, and a magnificent view of Marina del Rey.

The goal of the Unix Consultant is to provide a natural language help facility that allows new users to learn operating systems conventions in a relatively painless way. UC is not meant to be a substitute for a good operating system command interpreter, but rather, an additional tool at the disposal of the new user, to be used in conjunction with other operating system components.

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.

"Analysis of the job shop scheduling domain has indicated that the crux of the scheduling problem is the determination and satisfaction of a large variety of constraints. Schedules are influenced by such diverse and conflicting factors as due date requirements, cost restrictions, production levels, machine capabilities and substitutability, alternative production processes, order characteristics, resource requirements, and resource availability. This paper describes ISIS, a scheduling system capable of incorporating all relevant constraints in the construction of job shop schedules. We examine both the representation of constraints within ISIS, and the manner in which these constraints are used in conducting a constraint-directed search for an acceptable schedule. The important issues relating to the relaxation of constraints are addressed. Finally, the interactive scheduling facilities provided by ISIS are considered." Expert Systems 1(1):25-49.

Simulation and expert systems are remarkably similar. Both employ various representations to model some aspect of an uncertain world, with the model being formed as a piece of com puter software. This is then employed to aid decision making. Ideas about combining simulation and expert systems are presented, and a taxonomy is developed. It is concluded that the most fruitful areas of cross-fertilization are advice-giving ex pert systems that assist the simulation scientist and simulation user, new simulation tools built from knowledge-based tools, and intelligent front ends for simulation packages. Advice-giving systems will increasingly be part of simulation environments, rather than stand alone.

Totowa, N.J.: Rowman & Allanheld

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.

Totowa, N.J.: Rowman & Allanheld

The growing demand for systems of ever-increasing complexity and precision has stimulated the need for higher level concepts, tools, and techniques in every area of Computer Science. Some of these areas, in particular Artificial Intelligence, Databases, and Programming Lan guages, are attempting to meet this demand by defining a new, more abstract level of system description. We call this new level conceptual in recognition of its basic conceptual nature. In Artificial Intelligence, the problem of designing an expert system is seen primarily as a problem of building a knowledge base that repre sents knowledge about an enterprise. Consequently, Knowledge Repre sentation is viewed as a central issue in Artificial Intelligence research.

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.