Theory and Application of Expert Systems in Emergency Management Operations

Total Page:16

File Type:pdf, Size:1020Kb

Theory and Application of Expert Systems in Emergency Management Operations A11102 blS.TflE NATL INST OF STANDARDS & TECH R.I.C. A1 1102615982 /Theory and application of expert system QC100 .U57 N0.717 V1986 C.2 C.1 NBS-PUB- in NBS SPECIAL PUBLICATION 717 a % T / U.S. DEPARTMENT OF COMMERCE/National Bureau of Standards Theory and Application of Expert Systems in Emergency Management Operations Gass and Chapman, Editors . Tm he National Bureau of Standards' was established by an act of Congress on March 3, 1901. The M Bureau's overall goal is to strengthen and advance the nation's science and technology and facilitate their effective application for public benefit. To this end, the Bureau conducts research and provides: (1) a basis for the nation's physical measurement system, (2) scientific and technological services for industry and government, (3) a technical basis for equity in trade, and (4) technical services to promote public safety. The Bureau's technical work is performed by the National Measurement Laboratory, the National Engineering Laboratory, the Institute for Computer Sciences and Technology, and the Institute for Materials Science and Engineering The National Measurement Laboratory 2 Provides the national system of physical and chemical measurement; • Basic Standards coordinates the system with measurement systems of other nations and • Radiation Research furnishes essential services leading to accurate and uniform physical and • Chemical Physics chemical measurement throughout the Nation's scientific community, in- • Analytical Chemistry dustry, and commerce; provides advisory and research services to other Government agencies; conducts physical and chemical research; develops, produces, and distributes Standard Reference Materials; and provides calibration services. The Laboratory consists of the following centers: The National Engineering Laboratory Provides technology and technical services to the public and private sectors to Applied Mathematics address national needs and to solve national problems; conducts research in Electronics and Electrical 2 engineering and applied science in support of these efforts; builds and main- Engineering tains competence in the necessary disciplines required to carry out this Manufacturing Engineering research and technical service; develops engineering data and measurement Building Technology capabilities; provides engineering measurement traceability services; develops Fire Research 2 test methods and proposes engineering standards and code changes; develops Chemical Engineering and proposes new engineering practices; and develops and improves mechanisms to transfer results of its research to the ultimate user. The Laboratory consists of the following centers: The Institute for Computer Sciences and Technology Conducts research and provides scientific and technical services to aid Programming Science and Federal agencies in the selection, acquisition, application, and use of com- Technology puter technology to improve effectiveness and economy in Government Computer Systems operations in accordance with Public Law 89-306 (40 U.S.C. 759), relevant Engineering Executive Orders, and other directives; carries out this mission by managing the Federal Information Processing Standards Program, developing Federal ADP standards guidelines, and managing Federal participation in ADP voluntary standardization activities; provides scientific and technological ad- visory services and assistance to Federal agencies; and provides the technical foundation for computer-related policies of the Federal Government. The In- stitute consists of the following centers: The Institute for Materials Science and Engineering Conducts research and provides measurements, data, standards, reference Ceramics materials, quantitative understanding and other technical information funda- Fracture and Deformation mental to the processing, structure, properties and performance of materials; Polymers addresses the scientific basis for new advanced materials technologies; plans Metallurgy research around cross-country scientific themes such as nondestructive Reactor Radiation evaluation and phase diagram development; oversees Bureau-wide technical programs in nuclear reactor radiation research and nondestructive evalua- tion; and broadly disseminates generic technical information resulting from its programs. The Institute consists of the following Divisions: 'Headquarter! and l aboratories al Gaithersburg, MD, unless otherwise noted; mailing address Gaithersburg, MD 20899. : Some divisions within the center are located at Boulder, CO 80303. 'l (x-.ii'.'cl ai Boulder, CO, with some elements at Gaithersburg, MD. Theory and Application of Expert Systems in Emergency Management Operations NBS Proceedings of a Symposium RESEARCH INFORMATION Held at the Department of Commerce CENTER Washington, DC April 24-25, 1985 (Xuoo ,uXi Edited by: Saul I. Gass College of Business and Management 12, University of Maryland College Park, Maryland 20742 and Robert E. Chapman National Engineering Laboratory National Bureau of Standards Gaithersburg, Maryland 20899 Sponsored by: Federal Emergency Management Agency 500 C Street, S.W. Washington, DC 20472 and Center for Applied Mathematics National Engineering Laboratory National Bureau of Standards Gaithersburg, Maryland 20899 U.S. DEPARTMENT OF COMMERCE, Malcolm Baldrige, Secretary NATIONAL BUREAU OF STANDARDS, Ernest Ambler, Director Issued November 1986 Library of Congress Catalog Card Number: 86-600546 National Bureau of Standards Special Publication 717 Natl. Bur. Stand. (U.S.), Spec. Publ. 717, 264 pages (Nov. 1986) CODEN: XNBSAV U.S. GOVERNMENT PRINTING OFFICE WASHINGTON: 1986 For sale by the Superintendent of Documents, U.S. Government Printing Office, Washington, DC 20402 Federal Emergency Management Agency Washington, D.C. 20472 Foreword by The Honorable Julius W. Becton, Jr. Director Federal Emergency Management Agency As the recently appointed director of the Federal Emergency Management Agency (FEMA), I would like to reinforce the thought-provoking material contained in these Proceedings of the Symposium on the Theory and Application of Expert Systems in Emergency Management Operations. As pointed out in the symposium, expert systems hold unique advantages in the arena of emergency management. These systems can give you advice quicker, better and more extensively than panicked human beings. Quicker, because they can be consulted at any hour by using a personal computer. Better, because they contain the distilled opinion of a wide range of expertise. More extensively, because the computer can simultaneously consider a wider range of input factors than any human being. And the computer never panics, seldom sleeps, and has never been known to be absent. In the time that has passed since the symposium, FEMA has taken some of the first steps indicated by the symposium. We now have available on our computer or under development expert systems in such diverse areas as notification of emergency weather conditions and estimation of facility damage in earthquakes. The technology is being used to codify emergency expertise, and to improve our emergency management capabilities. The future is limited only by our imagination. One can imagine the ability to call a number and to be answered by a computer that can understand your speech. This computer would have a suite of expert systems which it would relate to your description of your problem, and give you the best advice available. The ability to perform smarter with less is and will continue to be a driving force. The publication of this document is evidence of our continued commitment to excellent research devoted to fulfilling human needs. i i i ABSTRACT The First Symposium on The Theory and Application of Expert Systems in Emergency Management, held at the Department of Commerce, Washington, DC (April 24 and 25, 1985) was funded by the Federal Emergency Management Agency and organized by the National Bureau of Standards' Operations Research Division. The purpose of the symposium was to bring together researchers in expert systems, artificial intelligence, and emergency operations in a forum to review the concepts of expert systems and the problems of emergency management, with the objective of determining how expert systems can be used to augment the experience of local, State and Federal emergency managers faced with the difficult tasks of determining the best response to an emergency situation. Speakers addressed the following areas: The theory and uncertainty aspects of expert systems, artificial intelligence's future role in emergency management, technology for building and using expert systems, emergency management decisions and information needs and uses, applications of expert systems in the management of chemical spills and shipboard and coal mine fires, and the role and use of simulation in emergency management expert systems. Keywords: Artificial intelligence; chemical spills; coal mine fires; decision support systems; emergency management; expert systems; fire management; shipboard fires; simulation; uncertainty Note: The paper "Emergency Management for Chemical Spills" by C. K. Johnson that was presented at the Symposium is not included in this volume. The interested reader should consult "Emergency Management of Inland Oil and Hazardous Chemical Spills: A Case Study in Knowledge Engineering," Chapter 10 in Bui ldi ng Expe r t System , F. Hayes-Roth, D. A. Waterman and Douglas B. Lenat (eds."J"» Addi son-Wesl ey Publishing Company, Inc., 1983. i v TABLE OF CONTENTS Page Foreword -- Julius
Recommended publications
  • Artificial Intelligence in Health Care: the Hope, the Hype, the Promise, the Peril
    Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril Michael Matheny, Sonoo Thadaney Israni, Mahnoor Ahmed, and Danielle Whicher, Editors WASHINGTON, DC NAM.EDU PREPUBLICATION COPY - Uncorrected Proofs NATIONAL ACADEMY OF MEDICINE • 500 Fifth Street, NW • WASHINGTON, DC 20001 NOTICE: This publication has undergone peer review according to procedures established by the National Academy of Medicine (NAM). Publication by the NAM worthy of public attention, but does not constitute endorsement of conclusions and recommendationssignifies that it is the by productthe NAM. of The a carefully views presented considered in processthis publication and is a contributionare those of individual contributors and do not represent formal consensus positions of the authors’ organizations; the NAM; or the National Academies of Sciences, Engineering, and Medicine. Library of Congress Cataloging-in-Publication Data to Come Copyright 2019 by the National Academy of Sciences. All rights reserved. Printed in the United States of America. Suggested citation: Matheny, M., S. Thadaney Israni, M. Ahmed, and D. Whicher, Editors. 2019. Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril. NAM Special Publication. Washington, DC: National Academy of Medicine. PREPUBLICATION COPY - Uncorrected Proofs “Knowing is not enough; we must apply. Willing is not enough; we must do.” --GOETHE PREPUBLICATION COPY - Uncorrected Proofs ABOUT THE NATIONAL ACADEMY OF MEDICINE The National Academy of Medicine is one of three Academies constituting the Nation- al Academies of Sciences, Engineering, and Medicine (the National Academies). The Na- tional Academies provide independent, objective analysis and advice to the nation and conduct other activities to solve complex problems and inform public policy decisions.
    [Show full text]
  • The History of Expert Systems
    Articles Learning from Artificial Intelligence’s Previous Awakenings: The History of Expert Systems David C. Brock n Much of the contemporary moment’s f it is indeed true that we cannot fully understand our enthusiasms for and commercial inter- present without knowledge of our past, there is perhaps ests in artificial intelligence, specificial- Ino better time than the present to attend to the history of ly machine learning, are prefigured in artificial intelligence. Late 2017 saw Sundar Pichai, the CEO the experience of the artificial intelli- of Google, Inc., opine that “AI is one of the most important gence community concerned with expert systems in the 1970s and 1980s. This things that humanity is working on. It’s more profound than, essay is based on an invited panel on I don’t know, electricity or fire” (Schleifer 2018). Pichai’s the history of expert systems at the notable enthusiasm for, and optimism about, the power of AAAI-17 conference, featuring Ed multilayer neural networks coupled to large data stores is Feigenbaum, Bruce Buchanan, Randall widely shared in technical communities and well beyond. Davis, and Eric Horvitz. It argues that Indeed, the general zeal for such artificial intelligence sys- artifical intelligence communities today tems of the past decade across the academy, business, gov- have much to learn from the way that ernment, and the popular imagination was reflected in a earlier communities grappled with the New York Times Magazine , issues of intelligibility and instrumen- recent article “The Great AI Awak- tality in the study of intelligence. ening” (Lewis-Kraus 2016). Imaginings of our near-future promoted by the World Economic Forum under the banner of a Fourth Industrial Revolution place this “machine learn- ing” at the center of profound changes in economic activity and social life, indeed in the very meaning of what it means to be human (Schwab 2016).
    [Show full text]
  • An Overview of Expert Systems
    NBS NAT'L INST. OF STAND & TECH U.S. Department publications of Commerce National Bureau AlllDb EE52SE of Standards NBSIR 82-2505 AN OVERVIEW OF EXPERT SYSTEMS May 1982 Prepared for — lilC " National Aeronautics and Space inn Administration Headquarters Washington, D.C. 20546 o2-2iUb lyo2 c. 2 MATIUNAL UUKaAb OP BTANOABUS UBtAJtT NBSIR 82-2505 JUL 7 1^2 AN OVERVIEW OF EXPERT SYSTEMS A - - - - d I William B. Gevarter U.S. DEPARTMENT OF COMMERCE National Bureau of Standards National Engineering Laboratory Center for Manufacturing Engineering Industrial Systems Division Metrology Building, Room A127 Washington, DC 20234 May 1982 Prepared for: National Aeronautics and Space Administration Headquarters Washington, DC 20546 U.S. DEPARTMENT OF COMMERCE, Malcolm Baldrige, Secretary NATIONAL BUREAU OF STANDARDS, Ernest Ambler, Director i s\n>»\i’i aiwujfA*!, \i. t.f.iMA0»ATa "SO TRAsnrw SBer t JUl m ^ :'^;. 'r 4 i /_ !^-., ^ -A- i ii'.i Uf »tSs»-®:'»airJ ( ^' ^n<3l^iyl0 -4^W: ^ ' ' 'ift’tm .'£f 1^ '''t m': t •«•, !©• u 3i ^'i:. #' 'T @|» la.. '• >S' \n "4kv 'r^-trii 1. /2r„ a. li 3 I GS^ m m wai I ft % ^ i <*sf '-.-4 Preface Expert systems is probably the "hottest” topic in Artificial Intelligence (AI) today. In the past, in trying to find solutions to problems, AI researchers tended to rely on search techniques or computational logic. These techniques were successfully used to solve elementary or toy problems or very well structured problems such as games. However, real complex problems are prone to have the characteristic that their search space tends to expand exponentially with the number of parameters involved.
    [Show full text]
  • An Introduction to Expert Systems
    AN INTRODUCTION TO EXPERT SYSTEMS by Bryan S. Todd Technical Monograph PRG·95 ISBN 0-902928-73-2 February 1992 Oxford University Computing Laboratory Programming Research Group 11 Keble Road Oxford OXI3QD England Copyri~ht © 1992 Brya.n S. Todd Oxford University Computing Laboratory Programming Research Group 11 Keble Road Oxford OX! 3QD England Electronic mail: toddlDcomlab.ox.ac.uk An Introduction to Expert Systems Bryan S. Todd Abstract This monograph provides an introduction to the theory of expert systems. The task of medical diagnosis is used as a unifying theme throughout. A broad perspective is taken, ranging from the role of diagnostic programs to methods of evaluation. While much emphasis is placed on probability theory, other calculi of uncertainty are given due consideration. Contents 1 Synopsis 1 1.1 Scope of Monograph 1 1.2 Outline of }'·1onograph 2 2 Decision Support Systems 3 2.1 Purpose a.nd Role . 3 2.1.1 Checklists . 3 2.1.2 Decision Aids 3 2.2 Early Attempts . 4 2.2.1 Flowcharts 5 2.3 Observer Variation . 5 2.4 Statistical Methods. G 2.4.1 The Value of Raw Data G 2.4.2 Probability Theory G 2.4.3 Bayes' Theorem 10 3 Data-Based Approaches 13 3.1 Validity of the Independence Assumption 13 3.2 Avoiding the Independence Assumption 14 3.2.1 Lancaster Model .. 14 3.2.2 Clustering Methods 14 3.2.3 Kernel Method ... 14 3.3 Nearest-Neighbours Method 15 3.4 Logi stie Model ...... 16 3.4.1 The Spicgelhalt€r-Knill~Jones Method 17 3.5 Recursive Partitioning 19 3.6 Neural Networks 22 4 Rule-Based Methods 24 4.1 Types of Knowledge ..
    [Show full text]
  • An Expert System for Diagnosis of Blood Disorder
    International Journal of Computer Applications (0975 – 8887) Volume 100– No.7, August 2014 An Expert System for Diagnosis of Blood Disorder Ayangbekun, Oluwafemi J Olatunde, Ayoola I. Felix O. Bankole Department of Information Department of Computer Department of Information Systems Science Systems University of Cape Town Crescent University Abeokuta University of Western Cape South Africa Nigeria South Africa ABSTRACT diagnosis/treatment. The proposed system has been subjected The mortality rates in medical sector have given birth to most to a voluminous amount of information gotten from the technology that aids in treatment of diverse ailments. The hematologist (i.e the expert in this field). The information is commencement of technology era in the 20 century gives a great relatively manipulated into expert system to solve humans’ mark to the limit of unnecessary element that causes mortality. As blood problems. Most expert system build recently has left the time is one of the major elements that deal with mortality issue, issue of blood diagnosis and focuses on other major area of this however give birth to this research as it stands out to help in medicine. Although there is no singular system that can take reducing the time that is used in attending to a critical patient care of all medical works, in which as well, there has been medical condition. This is to aid the doctors and other medical nobody that can stand out to call himself an expert in all area practioners in attending and prescribing treatment to patients. of medicine which has always made this a little cumbersome.
    [Show full text]
  • A Knowledge-Based Expert Systems Primer and Catalog Abstract 1
    A Knowledge-Based Expert Systems PrImer and Catalog Bruce K. Hillyer Department of Computer Science Columbia University October 1985 CUCS-195-85 Abstract For more than 20 years, artificial intelligence techniques have been applied to the development of computer programs that solve difficult problems. Although several expert systems are well known, it is all too easy to circumscribe the field based on these few examples. The purpose of this paper is to present the fundamentals of the field (the Primer), and to give a broad overview via concise descriptions of many rule-based expert systems and knowledge engineering frameworks (the Catalog). 1 Introduction This paper is offered to persons who have some awareness of artificial intelligence techniques, and who would like to gain an overview of the rule-based expert systems field. The fundamental terminology and concepts are presented here, together with numerous concise descriptions of individual expert systems and tools for expert system constructlOn. AttentlOn is gIven to the manner in which knowledge is represented, and the ways inferencing is performed. Previously published articles have described seminal expert systems, elucidated general principles, and have presented techniques for constructing expert systems. Among these are [Davis and King, 1976; Feigenbaum, 1977; Hayes-Roth, \Vaterman, and Lenat, 1978; \Vaterman and Hayes-Roth, 1978; Buchanan, 1982; Ennis, 1982; Stefik et al., 1982; Buchanan and Duda, 1983; Duda and Shortliffe, 1983; Minker, 1983; Nau, 1983; Hayes-Roth, Waterman, and Lenat, 1983; Kobler, 1984; Hayes­ Roth, 1985]. The present paper differs from these in that it covers a greater number of systems, rather than treating a smaller number in depth.
    [Show full text]
  • AI in Medicine the Spectrum of Challenges from Managed Care to Molecular Medicine
    AI Magazine Volume 20 Number 3 (1999) (© AAAI) Articles AI in Medicine The Spectrum of Challenges from Managed Care to Molecular Medicine Russ B. Altman, M.D., Ph.D I AI has embraced medical applications from its inference algorithms required. There was (and inception, and some of the earliest work in suc- is) considerable debate about how complex cessful application of AI technology occurred in inference should be for expert performance, medical contexts. Medicine in the twenty-first cen- but it is clear that medicine is a field in which tury will be very different than medicine in the there is a lot of knowledge required for good late twentieth century. Fortunately, the technical performance. It is also clear that physicians challenges to AI that emerge are similar, and the prospects for success are high. constantly feel a sense of overload as they deal with the individual data associated with their patients as well as the content knowledge of hen I was asked to make this presen- medicine that they are trying to apply to the tation, the organizers specifically treatment of these patients. I can try to provide Wasked me to review a bit of the histo- a feel for the information-processing load on a ry of AI in medicine (AIM) and to provide an physician: A full-time general practitioner is update of sorts. I have therefore taken the lib- currently expected to longitudinally follow a erty of dividing the last 30 years of medical AI panel of 2000 to 2500 patients. Of course, the research into three eras: the era of diagnosis, severity of illness varies, but it is clear that the era of managed care, and the era of molec- physicians need systems (computer or other- ular medicine.
    [Show full text]
  • AI Algorithms, Data Structures, and Idioms in Prolog, Lisp, and Java
    AI Algorithms, Data Structures, and Idioms in Prolog, Lisp, and Java Luger_all_wcopyright_COsfixed.pd1 1 5/15/2008 6:34:39 PM Luger_all_wcopyright_COsfixed.pd2 2 5/15/2008 6:34:39 PM AI Algorithms, Data Structures, and Idioms in Prolog, Lisp, and Java George F. Luger William A. Stubblefield Luger_all_wcopyright_COsfixed.pd3 3 5/15/2008 6:34:39 PM Executive Editor Michael Hirsch Acquisitions Editor Matt Goldstein Editorial Assistant Sarah Milmore Managing Editor Jeff Holcomb Digital Assets Manager Marianne Groth Senior Media Producer Bethany Tidd Marketing Manager Erin Davis Senior Author Support/ Technology Specialist Joe Vetere Senior Manufacturing Buyer Carol Melville Text Design, Composition, and Illustrations George F Luger Cover Design Barbara Atkinson Cover Image © Tom Barrow Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and Addison-Wesley was aware of a trademark claim, the designations have been printed in initial caps or all caps. Copyright © 2009 Pearson Education, Inc. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America. For information on obtaining permission for use of material in this work, please submit a written request to Pearson Education, Inc., Rights and Contracts
    [Show full text]
  • The Designing and Applications of Expert Computer Systems in the Sciences
    International Journal of Latest Trends in Engineering and Technology (IJLTET) ISSN: 2278-621X The Designing and Applications of Expert Computer Systems in the Sciences Parag Rastogi Assistant Professor Department of Information Technology Bharat Institute of Technology, Meerut(UP) INDIA Abstract- The credibility of AI rose to new heights in the minds of individuals and critics when many Expert Systems(ES) were successfully planned, developed, and implemented in many challenging areas. As of today, quite a heavy investments is done in this sector. The success of these programs in very selected fields involving high technical expertise has left people to explore new avenues. An expert computer system is a computer program which captures the knowledge of a human expert on a given problem, and applies this knowledge to solve complex problems in a manner similar to the expert. This paper describes important concepts pertaining to the expert computer systems, highlights the major characteristics, structure and design methodology of the expert systems. This paper also reviews the different applications of expert systems in various classes of science. Keywords – expert computer systems, heuristic-knowledge, inference engine, knowledge base, knowledge acquisition. I. INTRODUCTION One of the goals of AI is to understand the concept of intelligence and develop intelligent computer programs. The most important applied area of AI is the field of expert systems. An Expert System (ES) is a knowledge based system that employs knowledge about its application domains and uses an inferencing (reasoning) procedure to solve problems that would otherwise require human competence or expertise. The power of expert systems stems primarily from the specific knowledge about a narrow domain stored in the expert system’s knowledge base.
    [Show full text]
  • Case Studies of Expert System & Research Aspects in Artificial Intelligence
    CASE STUDIES OF EXPERT SYSTEM & RESEARCH ASPECTS IN ARTIFICIAL INTELLIGENCE Amanpreet Kaur Assistant Professor, Computer Science, Guru Nanak College, For Girls Sri Muktsar Sahib ABSTRACT An expert system is a computer program that represents and reasons with knowledge of some specialist subject with a view to solving problems or giving advice. An Expert System is a problem solving and decision making system based on knowledge of its task and logical rules or procedures for using knowledge. Both the knowledge and the logic are obtained from the experience of a specialist in the area. To solve expert-level problems, expert systems will need efficient access to a substantial domain knowledge base, and a reasoning mechanism to apply the knowledge to the problems they are given. They will generally build upon the ideas of knowledge representation, production rules, search, and so on.Often we use an expert system shell which is an existing knowledge independent framework into which domain knowledge can be inserted to produce a working expert system. This paper introduces introduction, structure, new tools and applications of expert system. In this paper advantages and limitations of expert system. This paper also outlined current research trends which are going on expert system. Keywords: Knowledge base,Inference Engine,PUFF,DENDRAL,MYCIN I. INTRODUCTION An Artificial Intelligence System created to solve problems in a particular domain is called an Expert System. An expert system is a knowledge intensive program to solve the problem in a domain that require considerable amount of technical expertise. An expert system is computer program that simulates the judgment and behavior of a human or an organization that has expert knowledge and experience in a particular field.
    [Show full text]
  • The History of Artificial Intelligence at Rutgers
    AI Magazine Volume 6 Number 3 (1985) (© AAAI) Introduction to the Comtex Microfiche Edition of the Rutgers University Artificial Intelligence Research Reports: The History of Artificial Intelligence at Rutgers Saul Amarel Background and Overview was formed in 1977 as the research arm of DCS under my direction. Work in LCSR covers all areas of Computer The founding of a new College at Rutgers in 1969 became Science ~with Al continuing to be the dominant area of the occasion for building a strong Computer Scicncc pres- activity. About fifty people wit,11 Rutgers appointments- ence in the University. Livingston College thus provided faculty, graduate students and technical staff-and twenty the home for the newly organized Department of Com- five collaborators in other institutions are currently in- puter Science (DCS) and for the beginning of Computer volved in various aspects of Al research in LCSR. Science research at Rutgers. 1 came to chair the depart,- The historical highlights of the development of Al re- ment after ten years at RCA Labs in Princeton, where 1 search at Rutgers, with emphasis on the major strands headed the Computer Theory Research group. My own and themes that brought us to the current state of our re- work in the Labs concentrated mainly in Al. In the early search, naturally reflects my personal perspective on this 196Os, 1 became interested in problems of representation development. in problem solving, and in computer methods for building The prcscnt collection of reports covers most of our models and solving formation problems. As 1 continued work in Al from 1970 through 1983.
    [Show full text]
  • The Applied Side of Artificial Intelligence
    Knowledge Engineering: The Applied Side of Artificial Intelligence EDWARD A. FEIGENBAUM HEURISTIC PROGRAMMING PROJECT COMPUTER SCIENCE DEPARTMENT STANFORD UNIVERSITY CALIFORNIA 94305 Copyright: New York Academy of Sciences, 1984 STANFORD, 1 Table of Contents 1 Introduction: Symbolic Computation and Inference 1 1.1 Knowledge 1 1.2 Expert Systems 1 1.3 The Scientific Issues UnderlyingKnowledge Engineering 2 2 A BriefTutorial Using the MYCIN Program 3 2.1 Knowledge in MYCIN 3 2.2 Inexact Inference 4 2.3 MYCIN Diagnosis and Therapy 5 2.4 MYCIN'SLine-of-Reasoning 6 2.5 MYCIN'S Inference Procedure ' 7 3 Building a New System with the EMYCINTool: PUFF , 8 4 Another Application of the EMYCIN Tool 9 5 Concluding Remarks on MYCIN-like Systems 11 6 Hypothesis Formation and Theory Formation: DENDRAL and META-DENDRAL 12 6.1 DENDRAL's Knowledge and Method 12 6.2 DENDRAL's Applications . 13 6.3 KnowledgeAcquisition 13 7 Knowledge Acquisition, Discovery, Conjecturing: AM 15 8 Two Major Principles of Knowledge Engineering 16 9 ThePromise ofKnowledge Engineering '" 17 9.1 Cost Reductions 17 9.2 The Inevitability Argument 18 9.3 The Most Important Gain: New Knowledge ' 18 10Problems ofKnowledge Engineering * 19 10.1TheLack of Adequateand Appropriate Hardware 19 10.2Lack of Cumulation of AI Methods and Techniques ' 19 10.3 Shortage ofTrainedKnowledge Engineers 20 10.4 TheProblem of Knowledge Acquisition 21 10.5TheDevelopment Gap 21 11 Acknowledgments 22 % ' 1 1 Introduction: Symbolic Computation and Inference This paper will discuss the applied artificial intelligence work that is sometimes called- "knowledge engineering". The work is based on computer programs that do symbolic manipulations and symbolic inference, not calculation.
    [Show full text]