The Handbook of Artificial Intelligence: Volume 2 by Avron Barr (PDF)

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The Handbook of Artificial Intelligence, Volume II focuses on the improvements in artificial intelligence (AI) and its increasing applications, including programming languages, intelligent CAI systems, and the employment of AI in medicine, science, and education. The book first elaborates on programming languages for AI research and applications-oriented AI research. Discussions cover scientific applications, teiresias, applications in chemistry, dependencies and assumptions, AI programming-language features, and LISP. The manuscript then examines applications-oriented AI research in medicine and education, including ICAI systems design, intelligent CAI systems, medical systems, and other applications of AI to education. The manuscript explores automatic programming, as well as the methods of program specification, basic approaches, and automatic programming systems. The book is a valuable source of data for computer science experts and researchers interested in conducting further research in artificial intelligence.

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐This is the second volume of a 3-volume set summarizing what was known at the time in the field of artificial intelligence (A.I.) Most of the content of this book is now called GOFAI, for “good-ole fashioned artificial intelligence”, but many of the algorithms and concepts discussed in this book are still used today, making this book still useful. This volume is centered more on applications of A.I. to the sciences, robotics, and language translation. In the introduction to the first volume one can find the following statement, which was true back then and dramatically more so at the present time: “There is every indication that useful A.I. programs will play an important part in the evolving role of computers in our lives-a role that has changed, in our lifetimes, from remote to commonplace and that, if current expectations about computing cost and power are correct, is likely to evolve further from useful to essential.” One can at the present time project this statement out into the near future with confidence, due to the accelerating advances in A.I. that have taken place since this volume was written. Indeed, there is a collection of A.I. researchers that believe that intelligent machines will surpass human capabilities by many orders of magnitude by the end of the next thirty years. This is an extremely optimistic prediction but it looks hopeful. The prospect that intelligent, autonomous machines will be living among us very soon is indeed an exciting one. Chapter six discusses the programming languages used in A.I. research. The language LISP was the predominant one used at the time of publication, but now PROLOG has made significant inroads, but is only briefly discussed in the book. LISP and a few other programming languages are mentioned in the book, and a comparison of their strengths and abilities to do certain tasks is done. I have only used LISP and Prolog so I cannot speak of the capabilities of the other languages discussed. The power of LISP to do recursion is one of its strongest points, and this chapter emphasizes this, along with its ability to parallel control and data structures, and its ability to think of programs as data. There is also an interesting discussion in this chapter on “dependency records”, which are used to keep track of the steps taken in a reasoning program. An intelligent program or machine must be able to explain their conclusions in terms of the information given. In chapter seven one finds a fairly extensive discussion of how A.I. has been applied to scientific research. My first project in A.I. thirteen years ago was to build an expert tutor for physics students and this chapter assisted me to a large degree in accomplishing this. The examples given in the chapter are of course at the present time way out of date, since an explosion of A.I. applications to science and medicine have occurred in the last decade. For example, the chapter mentions MACSYMA as being a package to do symbolic integration. There are now a few of these on the market, such as Maple and Mathematica, and interestingly, their ability to do symbolic integration is not considered to be a manifestation of artificial intelligence, as it is in this volume. In many cases, capabilities of computers once considered “smart” are now considered “routine”. This is an interesting trend, and if it continues, will assist in the public acceptance of artificial intelligence.Chapter eight details a few of the medical applications of A.I. that were being researched at the time of publication. This situation has increased dramatically since then, for now there are commercial companies whose sole product are programs, based on artificial intelligence, for diagnostics and prognostics. One of the systems discussed in this chapter is MYCIN, one of the earliest expert systems, and was used to diagnose and make recommendations for treatments of certain blood infections. The use of certainty factors by MYCIN is outlined, which are values between -1 and +1 that reflect the degree of belief in a hypothesis. The MYCIN project has generated a lot of refinements and improvements since its inception in the 1970’s. Current applications of A.I. in medicine emphasize neural networks for nonlinear prediction, genetic algorithms and evolutionary programming, disease modeling, and real-time clinical problem-solving. One of the primary successes of these approaches has been in the intelligent modeling or patients with multiple disorders.Chapter nine was the most useful chapter for me of the three volumes, for it discusses, among other things, how to create intelligent tutoring systems. My first foray into A.I. thirteen years ago involved creating, using LISP, a physics tutor for freshman physics students. The reading of this chapter was of great assistance in that it outlined the approaches taken at the time for using A.I. in education, which was labeled at the time as ICAI (intelligent computer-assisted instruction). The most difficult problem in designing an intelligent tutoring system is in dealing with the many (sometimes very creative) answers given by students. It is best to first deal with simple multiple-choice answers, which the program can then analyze relative to an extensive database. More powerful techniques are now available, coming from knowledge or “ontological” engineering, as it is sometimes called, and from natural language processing. Also, the advent of symbolic programming languages, such as Maple and Mathematica, make it easier to deal with the computational part of student answers, and these languages can even serve as a language in which to write the tutor. The ideal tutor must be able to assist the student in conceptual mistakes, as well as deal with blatantly wrong answers and arithmetic mistakes. The trend seems to be now in sophisticated digital assistants that employ all of these techniques and gigantic databases in order to reach this goal. These developments are very exciting, and will mesh well with the research and development in automatic scientific discovery.

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