Artificial intelligence

AltTnUCL~LINTELLIGn~CE

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Book Review Earl B. Hunt. Artificial Intelligence. Academic Press, New York, 1975, xii +468 pages, $29.00. Hunt's approach t o a n introductory AI text is summarized by his criticism that, "training in the artificial intelligence field, and to a lesser extent, in computer science in general, is too bound to fads. Too much efl'ort is placed on putting the student at the forefront of the field and too little in defi-dng and presenting basic information." So he has "tried to gather together, in one place, a reasonably detailed discussion of the basic mathematical and computational approaches to problems in the artificial intelligence field. Emphasis has been on principles, not details." (p. ix) His success in these aims is greatest in his presentation of pattern recognition. As the book proceeds, the amount of space devoted to each topic decreases and the ratio of presentation of example projects to discussion of basic principles increases. A good idea of the coverage of the book may be had from a list of the chapters and the number of pages in each: Part I Introduction Chapter I. The Scope of Artificial Intelligence 17 pp. II. Programming, Program Structure and Computability 25 pp. Part II Pattern Recognition Chapter IlL General Considerations in Pattern Recognition 12 pp, IV. Pattern Classification and Recognition Methods Based on Euclidean Description Spaces 43 pp. V. Non-Euclidean Parallel Procedures: The Perceptron 21 pp. VI. Sequential Pattern Recognition 21 pp. VII. Grammatical Pattern Classilication 39 pp. VIII. Feature Extraction 23 pp. Part HI Theorem Proving and Problem Solving Chapter IX. Computer Manipulable Representations in Problem Solving 15 pp. X. Graphic Representations in Problem Solving 30 pp. XI. Heuristic Problem-Solving Programs 34 pp. XH. Theorem Proving 55 pp. Artificialintelligence7 (1976), 199-201 Copyright © 1976by North-HollandPublishingCompany

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BOOK REVIEW

P "t IV Comprehension Chapter X!!I. Computer Preception XIV. Question Answering XV. Comprehension of Natural Language XVL Review and Prospectus

24pp. 41pp. 31 pp. 6pp.

The main weaknesses of this book as a text are its total lack of exercises for the student, its high price, its relative scarcity of detailed algorithms with virtually none in any programming language, and its numerous errors. Errors range from typos such as "Standord University" instead of"Stanford University" (p. 19); through errors due to sloppy or cheap pubfishing such as putting the caption of figure 4-8 on the facing page instead of compressing the figure slightly to fit the caption on the same page (pp. 80, 81), and omitting the graphics from Table 7-2 (p. 169); to errors of content attributable to the author, some of which will be discussed below. A ;Jst of errors is being sent to Hunt. Part II, on pattern ~ecognition, is the most successful part of the book as Hunt places a collection of numerical techniques in perspective for the AI type, who is oriented mostly to non-numeric symbol-manipulating. If Part H, together with Chapter XIII on computer perception, were published separately with exercises and a few more examples of applications, it would make an excellent text for a quarter to a third of a two semester introductory AI course. One error in this part is probably due to notation. Using Pr(A,B) for joint probability and Pr(A :B) for conditional probability, Bayes's rule is misstated as

H~Pr(H~) ~tPr(Ej:tla)

Pr(El: • Pr(HI'Ej) = o

instead of Pr(£j: Ht)Pr(H,) Pr(Ej : H,)Pr(H~ Pr(H," E,) = ZtftPr(Ei ' Ha) - X~ffitPr(E,: H,)Pr(H,)" Chapters IX-XI on problem solving present a good introduction, progressing through planning in GPS, STRIPS and PLANNER. Note, however, that the "acceptable thesis" theorem on p. 283 is misinterpreted (pp. 282-284). Chapter XII, on theorem proving, contains a very good, concise presentation of resolution theorem proving and a collection of strategies and heuristics. Two topics that might have been presented but weren't are Wang's algorithm and the transformation of a general wtf into prenex norraaI form. In fact, quantification is gi,~en barely three pages near the end of the chapter (Section 12.7). Delaying the discussion of quantiiiers works well except in Section 12.3,5 which consists of an example proof of a theorem from group Artificial Intelligence 7 (1976), i99--201

BOOK REVIEW

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theory for which a Skolem function is needed. Instead of proving 3yYx (x x y = x), an unnecessaril~ • long proof is gh,en of 3y3x(x x y = x). The chapter on perception briefly d~scusses bott~ v~sual and speech perception and might better be placed in Part II. The chapters on question answering and c~mprehension of natural language offer a survey of the issues, but mainly e×amples from a few rather old programs. Section 14.1 on data structures Ibr question answering discusses only the data structure~ of BASEBALL, DEACON and PROTOSYNTHEX III. It does not discuss general semantic networks. The section ou deduction in question answering discusses nothing more recent than 1970. The chapter on natural language comprehension discusses ATN grammars without ever showing what a real edge looks like and never mentions case grammars, though it does briefly discuss Schank's conceptual networks and Winograd's use of Halliday's systemic gramm~r. In summary, this book is very good in some areas, somewhat weak in others. This ma~.s its high price more of a detriment than it would be if the book could have been used as the sole text for ~ two semester introductory. course. In comparison with the only other general introductory AI text, P. C. Jackson's Introduction to Artificial Intelligence (Petrocelli, New York, 1974), Hunt's book supplies some of the depth that Jackson's book lacks while Jackson includes exercises and presents a more complete and up to date survey° Hunt's stated approach is admirable, but he probably should not have attempted to cover the whole field. STUART C. SHAPIRO

Computer Science Department Indiana University Bloomington, Ind.

Artificial Intelligence 7 (1970, 199-201