DOCSLIB.ORG

Artificial Intelligence: Using Computers to Think About Thinking. Part 2

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Artificial Intelligence: Using Computers to Think About Thinking. Part 2 Artffkfd Isadlfgesw: Using Computers to Tlslnk about Thirskfng. Part 2. Some Pmctfcsd Applicatfom of AI Number 52 December 26, 1983 This is the second of a two-part essay liited success. Techniques developed on artificial intelligence (AI). Part 1 dis- for solving problems in one domain were cussed “knowledge representations”- usually inadequate for other domains. models of cognition that AI investigators However, programs equipped with a have used in their attempts to build great deal of information about a single thinking machines. 1 One of the most domain performed as well as, and some- ambitious goals of AI research is to de- times better than, experts in that field.z velop programs which enable machines The superior performance of these to perform commonsense reasoning. It knowledge-based, or expert, systems will & many years, however, before th~ convinced many AI researchers that goal is achieved. problem solving demands huge banks of In the meantime, AI research has knowledge as well as reasoning proce- spawned numerous spin-offs that are dures.3 just beginning to enter the commercial Today, the goal of expert systems re- market. These include programs that en- search is to transfer a specialist’s knowl- able robots to “see” and “feel” and ma- edge into a program so the information chines which can follow instructions can be efficiently accessed and used by written in natural language (NL). But the the computer to solve problems. This in- most ambitious and successful AI appli- cludes “textbook leaming’’—the facts cations thus far are “expert systems. ” obtained from training and reading.4 It These computer programs are designed also includes heuristic knowledge, or to duplicate the problem-solving pro- rules of thumb developed through years cesses of experts in various fields. This of experience and judgment. Heuristics essay will cover some of these expert sys- are essentially educated guesses about tems and review a liited number of which solutions to a problem are most other applications of AI. likely to be successful. They don’t guar- During the early years of AI research, antee correct answers. But they do save investigators were intent upon dMcover- time by limiting the search for solutions ing the general principles underlying in- to those most likely to be correct. Com- telligence. In developing the knowledge puter and other scientists called “knowl- representations described in Part 1, AI edge engineers” sometimes spend years researchers tried to use these principles picking experts’ brains for these facts to create an “inference engine.” Ideally, and heuristics, and then structure them such an engine would solve any type of into computer programs. problem, from winning at chess to d~ag- Expert systems also include “infer- nosing disease. But attempts at develop- ence procedures” that determine which ing computer-based problem-solving heuristics and facts should be brought to strategies for any situation met with bear on a problem. One such inference 430 procedure is backward chaiiing, in in modeliig and assisting scientific which you suggest a possible solution to thinkiig, and Lederberg, a geneti- a problem and work backward to see if cist/molecular biologist with expertise it’s correct. MYCIPJ, an expert system in chemistry, had developed a computer that assists in medical diagnoses, reasons language for describing the structure of in this manner.z It advances a disease complex molecules.g As the project hypothesis based on a few known symp- grew, Carl Djerassi, also at Stanford tom$. Then it looks for other symptoms Univemity, contributed his expertise. 10 which support the hypothesis, request- The original DENDRAL program per- ing additional tests and information as formed three basic operations on the needed. In most expert systems, the in- chemical formulas and mass spectral ference procedure for deciding which data with which it was provided. In the facts and heuristics to use is separate fmt phase, cafled “plan,” it translated from the knowledge base of facts and general and specific prior knowledge rules. This makes it easier to add or and heuristics into a spec~lc repertoire modify facts and rules as new informa- of constraints. In the next phase, called tion becomes available. “generation,” it generated plausible The fret, and probably best-known, structures based on such constraints as AI programs that focused on a Ihnited the number of rings, double bonds, and domain were chess programs. Donald atoms of various types in each molecule. Michie, University of Edinburgh, Scot- In the final phase, caUed “test,” each land, explains that chess is ideal for mod- plausible structure was tested. The com- eliig specialist knowledge because it is a puter fiit generated sets of instrument very well-defined domains A large data that would be expected to describe amount of formal information is avail- each structure. Then it compared each able in the form of instructional works set of data to actual data about the com- and commentaries. And numerical pound. The closest fits were then ranked scales of performance are avaifable in for the user. the national and international rating In the past few years, scientists work- systems. Equally important, chess is a ing on the DENDRAL project have fo- game that calls on a wide range of cogni- cused most of their attention on the tive functions, from logical calculation planning and generation portion of the to imaginative thinking. The numerous program, and on making this portion chess-playing programs developed in the available to users. Called CONGEN, for 1950s and 1960s tested the proficiency constrained structure generation, this with which various knowledge represen- generator has been expanded to infer tations used facts and heuristics to solve plausible structures using a wide variety problems.s of instrumental data. In 1982. CONGEN By the mid- 1960s, AI researchers was made commercially avaifable began to expand beyond chess and puz- through the computer network Compu- zle playing, or what Edward A. Feigen- Serve. II baum, Stanford University, California, Another spin-off of DENDRAL is calls “toy problems, “G(p. 62) into practi- Mets-DENDRAL, a program that gen- cal problems. The fiist such project re- erates its own rules from mass spectral sulted in DENDRAL, an expert system data on chemical compounds. After re- that identifies the chemicaf structures of ceiving mass spectral data on a family of unknown compounds.7,8 DENDRAL compounds, Mets-DENDRAL gener- was launched at Stanford University in ates planning and test rules that describe 1966 by Feigenbaum and Joshua Leder- how these compounds fragment when berg, now president of Rockefeller Uni- studied with mass spectrometry. Some versity, New York. Both were interested of the roles generated by Meta- 431 DENDRAL duplicated those formulated solve problems in that domain. Figure 1 by expert chemists, while others were shows one of the 500 such rules used by entirely original. 1Z the infectious diseases expert system DENDRAL demonstrated that AI MYCIN. With MYCIN, a physician en- techniques could be used to solve real ters information about a patient into the problems within a Iiiited area of knowl- computer. The computer then searches edge.b Paradoxically, it also demon- for the rules that can be applied to this strated that it is easier to model the rea- information. If more information is soning processes of specialists than to needed, the computer will ask the physi- program the steps a chfld goes through cian to supply it. Then the rules for these in understanding language, or making additional data are applied. This process commonsense inferences.z This is continues until a diagnosis, and treat- because the facts and judgments an ex- ment, can be recommended. pert uses in making a decision are easier Since the acceptability of an expert to identify and categorize than are the system depends on the confidence with reasoning processes used for general which physicians can accept its sugges- problem solving. tions, MYCIN and several other consul- So far, the most successful expert sys- tation systems can provide explanations tems have been programs that weigh and of their reasoning processes. At the phy- balance evidence about data to deter- sician’s request, MYCIN will list the pro- mine how they should be categorized. duction rules it used in its diagnosis or Differential diagnosis, for example, is “a treatment recommendation, and cite classical medical example of such a references to the literature that support problem,”z according to Richard O. these rules. MYCIN will also explain Duda, Syntelligence, Menlo Park, Cali- why it has requested additional tests or fornia, and Edward H. Shortliife, Stan- other information.z An example of thk ford University School of Medicine. A explanation feature is shown in Figure 2, physician arrives at a diagnosis by evaluating a variety of symptoms and Ffgure 1: Sample MYCIN production rule. test results. Although this is a fairly complicated procedure, it is based on Mycfri identifiable facts and heuristics and, [f: 1) The infection which requires therefore, lends itself to computer therapy is meningitis, modeling. For this reason, and because And 2) The patient has evidence of a computers can consider many diseases serious skin or soft tissue infection, that physicians might not encounter in everyday practice, numerous expert And 3) Organisms were not seen on the systems have been designed to assist stain of the culture, doctors in diagnosing and treating disease.z Amf 4) The type of infection is bacterial, The knowledge representation used risen: most widely in these expert or “consulta- tion” systems is the “production rule” There is evidence that the organism approach, according to Wi~lam B, (other than those seen on cultures or Gevarter, National Aeronautics and smears) which might be causing the in- Space Administration, Washington, fection is staphyloccwcus-coagpos (.75) DC.
Load more

Recommended publications
  • Prominence of Expert System and Case Study- DENDRAL Namita Mirjankar, Shruti Ghatnatti Karnataka, India [email protected],[email protected]
    International Journal of Advanced Networking & Applications (IJANA) ISSN: 0975-0282 Prominence of Expert System and Case Study- DENDRAL Namita Mirjankar, Shruti Ghatnatti Karnataka, India [email protected],[email protected] Abstract—Among many applications of Artificial Intelligence, Expert System is the one that exploits human knowledge to solve problems which ordinarily would require human insight. Expert systems are designed to carry the insight and knowledge found in the experts in a particular field and take decisions based on the accumulated knowledge of the knowledge base along with an arrangement of standards and principles of inference engine, and at the same time, justify those decisions with the help of explanation facility. Inference engine is continuously updated as new conclusions are drawn from each new certainty in the knowledge base which triggers extra guidelines, heuristics and rules in the inference engine. This paper explains the basic architecture of Expert System , its first ever success DENDRAL which became a stepping stone in the Artificial Intelligence field, as well as the difficulties faced by the Expert Systems Keywords—Artificial Intelligence; Expert System architecture; knowledge base; inference engine; DENDRAL I INTRODUCTION the main components that remain same are: User Interface, Knowledge Base and Inference Engine. For more than two thousand years, rationalists all over the world have been striving to comprehend and resolve two unavoidable issues of the universe: how does a human mind work, and can non-people have minds? In any case, these inquiries are still unanswered. As humans, we all are blessed with the ability to learn and comprehend, to think about different issues and to decide; but can we design machines to do all these things? Some philosophers are open to the idea that machines will perform all the tasks a human can do.
    [Show full text]
  • Application of Artificial Intelligence Technology in Oncology
    cancers Review Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine Ryuji Hamamoto 1,2,3,*, Kruthi Suvarna 4, Masayoshi Yamada 1,5, Kazuma Kobayashi 1,2,3, Norio Shinkai 1,2,3, Mototaka Miyake 6, Masamichi Takahashi 1,7, Shunichi Jinnai 8 , Ryo Shimoyama 1 , Akira Sakai 1,3, Ken Takasawa 1,2 , Amina Bolatkan 1,2, Kanto Shozu 1 , Ai Dozen 1 , Hidenori Machino 1,2, Satoshi Takahashi 1,2, Ken Asada 1,2 , Masaaki Komatsu 1,2 , Jun Sese 1,9 and Syuzo Kaneko 1,2 1 Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; [email protected] (M.Y.); [email protected] (K.K.); [email protected] (N.S.); [email protected] (M.T.); [email protected] (R.S.); [email protected] (A.S.); [email protected] (K.T.); [email protected] (A.B.); [email protected] (K.S.); [email protected] (A.D.); [email protected] (H.M.); sing.monotonyfl[email protected] (S.T.); [email protected] (K.A.); [email protected] (M.K.); [email protected] (J.S.); [email protected] (S.K.) 2 Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan 3 Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan 4 Indian Institute of Technology Bombay, Powai, Mumbai 400 076, India; [email protected] 5 Department of Endoscopy, National Cancer Center
    [Show full text]
  • Carl Djerassi 1923‐2015
    SenD#7009 MEMORIAL RESOLUTION CARL DJERASSI 1923‐2015 Carl Djerassi, Professor Emeritus of Chemistry, novelist, playwright, patron of the arts and pioneer in the production of the oral contraceptive pill, died in his home in San Francisco on January 30. He was 91 years old. Djerassi was born in Vienna in 1923 to Samuel Djerassi and Alice Friedmann. He lived for periods in Sofia, Bulgaria and Vienna but with the rising Nazi threat, moved with his mother to the United States, arriving nearly penniless in 1939. Djerassi graduated at the age of 18 from Kenyon College where in his own words, he “became a chemist”. He earned a Ph.D. in chemistry from Wisconsin in 1945 and subsequently worked at CIBA, developing one of the first commercial antihistamines (Pyribenzamine). In 1949, Djerassi became associate director of research at Syntex in Mexico City. His research on steroids led to the synthesis of norethisterone and the first birth‐control pill, studies that have since transformed science and society. Djerassi joined the faculty at Wayne State University in 1952. In 1959, he joined the faculty at Stanford while for a time serving also as president of Syntex Research. In 1968, he founded Zoecon, a company directed at hormonal control of insect pests. Djerassi published over 1200 scientific manuscripts, encompassing seminal contributions to tools for structure elucidation including mass spectrometry, magnetic circular dichroism and optical rotatory dispersion. He also made pioneering contributions to our understanding of the biosynthesis of marine natural products. With Nobel Laureate Joshua Lederberg and computer scientist Edward Feigenbaum, he also devised a computer program called DENDRAL, one of the earliest uses of artificial intelligence for structure elucidation.
    [Show full text]
  • A Tribute to Carl Djerassi
    A Trfbute to Carl Djensssi: Reflections on a Remarkable Scientific Entrepreneur Number 42 October 18,1982 There has been much said about the Djerassi is best known to the public for scientflc entrepreneur. Ordinarily the h~ contribution to the development of term is applied to the scientist who has the birth control pill. But research in con- been successful in business, One thinks traception is only one aspect of his multi- of Thomas Edison or Edwin Land, faceted career. He is one of the giants of founder of Polaroid, among others, But modern organic chemistry. Carl has been there are also scientific entrepreneurs in a leader in analyzing or “elucidating” the the academic community. Many Cur- structures of complex organic molecules rent Contents@ (G@’ ) readers fall into and in applying these discoveries to the this category so I will not belabor the synthesis of pharmaceutically important point. My reason for mentioning these compounds. He is also responsible for two meanings of scientific entrepreneur major advances in the methodologies is to emphasize that we rarely find both used by organic chemists. Analytical kinds in one person. To maintain a cred- techniques which he helped develop, ible academic existence one needs enor- such as optical rotatory dispersion, circu- mous dedication and energy. To func- lar dichroism, and mass spectrometry, tion in a scientifically oriented business are now among the most widely used you need these qualities as well as signifi- took in organic chemistry. cant managerial competence. That rare I first met Djerassi back in 1959. We combination of qualities is found in my were introduced by Joshua Lederberg, friend Carl Djerassi.
    [Show full text]
  • How DENDRAL Was Conceived and Born. Joshua Lederberg Rockefeller University New York, N.Y
    How DENDRAL was conceived and born. Joshua Lederberg Rockefeller University New York, N.Y. As agreed with your organizers, this will be a somewhat personal history. They have given me permission to recall how I came to work with Ed Feigenbaum on DENDRAL, an exemplar of expert systems and of modelling problem-solving behavior. My recollections are based on a modest effort of historiography, but not a definitive survey of and search for all relevant documents. On the other hand, they will give more of the flow of ideas and events as they happened than is customary in published papers in scientific journals -- accounts so dry that Medawar lugubriously calls them fraudulent (43); cf. Merton & Zuckerman (44, 45, 61). These authors point out that the standard scientific publication is narrowmindedly devoted to the context of justification. The DENDRAL effort (along with much of medical informatics) is dedicated to discovery: should we use a different standard for its history? I hope it will be eventually possible to divert my colleagues from the more important work they do from day to day, and join me in a larger effort at historical research and informed consensus. My account is inevitably incomplete, especially about what others were thinking at a given moment. Built into the phenomenon of history, as soon as enough time has passed to enable some detachef judgment, the evidence becomes frail, and we become vulnerable to the myths we create. Understanding all of these limitations, I will no longer qualify every remark: it should be implicit that each is "to the best of LAY recollection / or/ as best as can be inferred from the fragmentary documentary recordn.
    [Show full text]
  • The Stanford Heuristic Programming Project: Goals and Activities
    AI Magazine Volume 1 Number 1 (1980) (© AAAI) Al Magazine 25 directed machine transformations of a checked-out and these topics provides new techniques and mechanisms for the debugged single-machine implementation. design and construction of knowledge bases, its explanation, and its use to solve problems. Continued work on these topics Packet Radio Terminal System Evaluation provides new techniques and mechanisms for the design and Tom Ellis and Steve Saunders construction of knowledge-based programs; experience Work intended to result in a demonstration-level portable gained from the actual construction of these systems then terminal to test and evaluate various solutions to the issues feeds back both (a) evaluative information of the ideas’ utility raised by extreme portability in the packet-radio environment. and (b) reports of quite specific problems and the ways in which they have been overcome, which may suggest some more general method to be tried in other programs. One of our long-range goals is to isolate AI techniques that The Stanford Heuristic Programming Project: are general, to determine the conditions for their use and to Goals and Activities build up a knowledge base about AI techniques themselves. Under support from ARPA, NIH/NLM, ONR, NSF, and industry, the HPP conducts research on five key scientific by the Staff of the Heuristic Programming Project problems of AI, as well as a host of subsidiary issues: The Heuristic Programming Project (HPP) of the Stanford 1. Knowledge Representation-How shall the knowledge University Computer Science Department is a laboratory of necessary for expert-level performance be represented about fifty people-faculty, staff, and graduate students- for computer use? How can one achieve flexibility in whose main goals are these: adding and changing knowledge in the continuous development of a knowledge base? Are there uniform .
    [Show full text]
  • HARMON on BPM, January 2021 Deepmind Is No Longer Playing
    Process I HARMON ON BPM, January 2021 DeepMind Is No Longer Playing Games Science has made a lot of progress in the last 50 years, and nowhere has it made more progress than in biochemistry. Starting with the discovery of the structure of DNA in 1953, our understanding of biochemistry, biology, and medicine has grown by leaps and bounds. Once DNA’s overall structure had been resolved in the 50s, biochemists proceeded to work out how the genes caused things to happen. Without going into a lot of detail, suffice to say that the DNA is transcribed to RNA, which then generates specific proteins, each made up of some combination of 20 different amino acids that cause various chemical actions in living organisms. As time has passed the focus, in many cases, has shifted from what genes an organism has to what proteins an organism can generate, and, even more specifically, what actions are caused by each specific protein. As the focus shifted to proteins, a specific problem arose. Even with a complete knowledge of the amino acids that make up a specific protein, the way the amino acids fit together in the molecule makes an important difference. Consider a protein that has two open sites for a specific amino acid. The amino acid could bind at either site, and knowing which specific site it has bonded with, in a specific protein, makes all the difference. When you consider that some protein molecules are made up of thousands of amino acids and there are many hundreds of different bonding possibilities for various amino acids, you begin to see the nature of the problem.
    [Show full text]
  • A Review on Dendral Expert System
    6 II February 2018 International Journal for Research in Applied Science & Engineering Technology (IJRASET) ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 6.887 Volume 6 Issue II, February 2018- Available at www.ijraset.com A Review on Dendral Expert System Firdous Ul Rashid1 1Department of CSE , Islamic University J&K. Abstract: Study in the area of artificial intelligence has given rise to the rapidly growing technology known as expert system. Expert system is an area of artificial intelligence which is having a huge impact on various fields of life. Expert system uses human knowledge to solve the complex problems in various areas as science, engineering, business, medicine, weather forecasting and the organizations employing the technology of expert system has seen an increase in the quality and efficiency. Expert system is computer software that emulates the decision making ability of a human expert. The expert system represents knowledge acquired from human expert as data or rules within the computer. These rules and data can be called upon to solve the complex problems. This paper gives an overview of the expert system namely Dendral that is used in medical sciences from its invention. Keywords: Dendral, Plan-generate-test paradigm, Knowledge Engineering, Heuristics , Knowledge base. I. INTRODUCTION Dendral was a project in artificial intelligence (AI) of the 1960s, and the computer software expert system that it produced. Its primary aim was to study hypothesis formation and discovery in science. For that, a specific task in science was chosen: help organic chemists in identifying unknown organic molecules, by analysing their mass spectra and using knowledge of chemistry.[1] It was done at Stanford University by Edward Feigenbaum, Bruce G.
    [Show full text]
  • Joshua Lederberg 1 9 2 5 — 2 0 0 8
    NATIONAL ACADEMY OF SCIENCES JOSHUA LEDERBERG 1 9 2 5 — 2 0 0 8 A Biographical Memoir by S. GAYLEN BRADLEY Any opinions expressed in this memoir are those of the author and do not necessarily reflect the views of the National Academy of Sciences. Biographical Memoir COPYRIGHT 2009 NATIONAL ACADEMY OF SCIENCES WASHINGTON, D.C. JOSHUA LEDERBERG May 23, 1925–February 2, 2008 BY S . GAYLEN BRADLEY OSHUA LEDERBERG’S PIONEERING work on genetic recombina- Jtion in bacteria propelled the field of molecular genetics into the forefront of biological and medical research. His discoveries became the foundation for genetic engineering, modern biotechnology, molecular biology and medicine, and gene therapy. After receiving the Nobel Prize in 1958 at the tender age of 33, Joshua Lederberg used his prestige and breadth of knowledge to engage the public and policy makers for five decades in discourses on the consequences of the applications of science. He recognized the potential of computers for the analysis of scientific data, and how the logical processes of scientific induction were applicable to the construction of intelligent computer programs. He collabo- rated with scientists across many disciplines to revolutionize the applications of computers to chemistry, medicine, and information technology. While president of Rockefeller University he continued his advocacy for scientific understanding,1 drew attention to the threat of emerging infectious diseases, and ultimately returned to the laboratory to explore the process of gene mutation. At the time of his death Joshua Lederberg was University Professor and president emeritus of Rockefeller University. He died from pneumonia February 2, 2008, at BIOGRA P HICAL MEMOIRS New York-Presbyterian Hospital.
    [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 MYCIN Experiments of the Stanford Heuristic Programming
    In the early stages of the development of any science different men confronting the same range of phenomena, but not usually all the same particular phenomena, describe and interpret them in different ways. What is surprising, and perhaps also unique in its degree to the fields we call science, is that such initial divergences should ever largely disappear. T. S. Kuhn, The Structure of Scientific Revolutions (International Encyclopedia of Unified Science, vol. II, no. 2). Chicago: University of Chicago Press, 1962. The philosopher’s treatment of a question is like the treatment of an illness. L. Wittgenstein, Philosophical Investigations, para. 255 (trans. G. E. M. Anscombe). New York: Macmillan, 1953. Every one then who hears these words of mine and does them will be like a wise man who built his house upon the rock; and the rain fell, and the floods came, and the winds blew and beat upon that house, but it did not fall, because it had been founded on the rock. And every one who hears these words of mine and does not do them will be like a foolish man who built his house upon the sand; and the rain fell, and the floods came, and the winds blew and beat against that house, and it fell; and great was the fall of it. Matthew 7:24-27 (Revised Standard Version) PART ONE Background 1 The Context of the MYCIN Experiments Artificial Intelligence (AI) is that branch of computer science dealing with symbolic, nonalgorithmic methods of problem solving. Several aspects of this statement are important for understanding MYCINand the issues discussed in this book.
    [Show full text]