In fact, there are enough opinions for four men. That is, the views advanced are contradictory. I agree with one of the A fifth answer is also advanced, but is immediately withdrawn. Roger Schanks, and disagree with the other three. Schank hoped that his article would start a debate on As & hank points out, this is unsatisfactory because it leads the issues he raised.

The book is edited by Philip Klahr and the late Donald A. Waterman, both of Rand Corporation. The papers are selected from RAND technical reports published from 1977 to 1985. The book is most valuable to people learning knowledge engineering. Four of the papers provide interesting glimpses at the problems involved in transforming knowledge about a domain into computer representations. In addition, the book contains one or two interesting papers for researchers in each of the areas of knowledge acquisition, reasoning with uncertainty, and distributed problem solving.

Early this year fifty people took an experimental course at Xerox PARC on knowledge programming in Loops During the course, they extended and debugged small knowledge systems in a simulated economics domain called Truckin Everyone learned how to use the Loops environment, formulated the knowledge for their own program, and represented it in Loops At the end of the course a knowledge competition was run so that the strategies used in the different systems could be compared The punchline to this story is that almost everyone learned enough about Loops to complete a small knowledge system in only three days. Although one must exercise caution in extrapolating from small experiments, the results suggest that there is substantial power in integrating multiple programming paradigms. We extend our special thanks to the course participants from Applied Expert Systems, Daisy Systems, ESL, Fairchild AI Lab, Lawrence-Livermore Laboratories, Schlumberger-Doll Research Laboratory, SRI International, Stanford University, Teknowledge, and Xerox Corporation Their participation and feedback are vital to the ongoing experimental process for simplifying the techniques of knowledge programming We enjoyed and will long remember their spirited involvement. As in many situations in life, pat solutions and simple mathematical models just aren't good enough. To cope with messiness, AI researchers have found that large amounts of problem-specific knowledge are usually needed.

The two-volume set entitled Knowledge-Based Systems (Volume 1, Knowledge Acquisition for Knowledge-Based Systems, 355 pp., and Volume 2, Knowledge Acquisition Tools for Expert Systems, 343 pp., Academic Press, San Diego, California, 1988), edited by B. R. Gaines and J. H. Boose, is an excellent collection of papers useful to both commercial practitioners of knowledge-based-systems development and research-oriented scientists at specialized centers or academic institutions. The set is the result of a call for papers to support the first American Association for Artificial Intelligence Knowledge Acquisition for Knowledge-Based Systems Workshop, held 3-7 November 1986 in Banff, Canada. Although the conference was held three years ago, these volumes are still timely and sorely needed. Few books dedicated to knowledge acquisition exist. The first volume, Knowledge Acquisition for Knowledge-Based Systems, begins with a paper whose title sounds appropriate: "An Overview of Knowledge Acquisition and Transfer" by the editor B. R. Gaines.

Editor: Having worked on real-time expert systems for several years, I read with interest the survey article "Real-Time Knowledge-Based Systems" by Thomas Laffey, et al. in the recent edition of AI Magazine. Indeed, the work of myself and my colleagues was mentioned, but the description of YES/L1 in the article was incorrect in several ways. Laffey and his coauthors incorrectly describe YES/L1 as being "implemented in OPS5 and MacLisp " They seem to be confusing YES/Ll, the shell, with YES/MVS was written in OPS5. YES/L1 is a compiled, data-driven language and an environment for developing interactive and real-time expert systems. The YES/L1 language is an integration of procedural and rule-based techniques.

Sentences for the 50,000 offenders vary from community work release and probation to lifelong incarceration. Tennessee was one of 38 states required by court order to improve prison conditions and reduce overcrowding; it is the target of over 300 inmate lawsuits each year. The new $14 million system is the largest and most comprehensive computer system ever developed in the field of corrections. Sentences C and D are consecutive to sentence B, and sentence B is consecutive to sentence A. C, and D of an offender, as shown in figure 1, it must be determined which sentence is not consecutive to any others. In this case, A is the sentence that must first be calculated because its dates do not depend on a previous sentence.

Most high-performance expert systems rely primarily on an ability to represent surface knowledge about associations between observable evidence or data, on the one hand, and hypotheses or classifications of interest, on the other. Although the present generation of practical systems shows that this architectural style can be pushed quite far, the limitations of current systems motivate a search for representations that would allow expert systems to move beyond the prevalent "symptom-disease" style. One approach that appears promising is to couple a rule-based or associational system module with some other computational model of the phenomenon or domain of interest. According to this approach, the domain knowledge captured in the second model would be selected to complement the associational knowledge represented in the first module. Simulation models have been especially attractive choices for the complementary representation because of the causal relations embedded in them (Brown & Burton, 1975; Cuena, 1983).

AI Magazine Volume 9 Number 3 (1988) ( AAAI) The first part of the book is intended to be an introduction to computational jurisprudence for both groups. It identifies issues critical to the purpose, behavior, knowledge sources, knowledge structures, and reasoning processes of expert legal systems. The second part implements a simple prototype system for a well-defined area of contract law and is more appropriate for experienced developers of knowledge-based systems. Law is a domain in which the experts are supposed to disagree, and lawyers must be able to argue either side of a case. A judge or juror must decide which argument is "best."

The Japanese Fif-t,h Generation Project appears to be only a stimulus for this book. Clearly it is an important stimulus, and the book describes it in considerable detail as it covers both technical and managerial/social aspects of the Japanese project. But the book goes much beyond a description and an evaluation of the Fifth Generation Project In building the background of the project's significance, the book describes the current state of work in artificial intelligence (AI) in the US and abroad, it outlines the history of AI, it, focuses on developments in Expert Systems, it comments on the social and political environment in which AI is growing, and it provides glimpses of the type of future that AI may help us to create. Also, the relative state of industrial development in Japan and t,he US are analyzed, and many observations are made about styles of planning, value systems, and attitudes to education in the two countries. The book conveys very well the sense of intellectual excitement that characterizes work in AI, and the variety of viewpoints (and concerns) within the field about the possible impact on our lives of mass-produced knowledge technology.

The importance of contextual reasoning is emphasized by various researchers in AI. (A partial list includes John McCarthy and his group, R. V. Guha, Yoav Shoham, Giuseppe Attardi and Maria Simi, and Fausto Giunchiglia and his group.) Here, we survey the problem of formalizing context and explore what is needed for an acceptable account of this abstract notion. Although the word context is frequently used in descriptions, explanations, and analyses of computer programs in these areas, its meaning is frequently left to the reader's understanding; that is, it is used in an implicit and intuitive manner. An example of how contexts may help in AI is found in McCarthy's (constructive) criticism (McCarthy 1984) of I wish honorable gentlemen would have the fairness to give the entire context of what I did say, and not pick out detached words (R. Cobden [1849], quoted in Oxford English Dictionary [1978], p. 902). The main motivation for studying formal contexts is to resolve the problem of generality in AI.