At a workshop held in Toulouse, France, in 1977, Gallaire, Minker, and Nicolas stated that logic and databases was a field in its own right. This was the first time that this designation was made. The impetus for it started approximately 20 years ago in 1976 when I visited Gallaire and Nicolas in Toulouse, France. In this article, I provide an assessment about what has been achieved in the 20 years since the field started as a distinct discipline. Prominent among developments was work by Levien and Maron (1965) and Kuhns (1967), and by Green and Raphael (1968a), who were the first to realize the importance of the Robinson (1965) resolution principle for databases.

A basic promise of AI research is that what we observe as human intelligence is in fact a computation either directly or as an emergent effect. An attempt at classifying and distinguishing types of AI researchers was to call them all either scruffy (those that wrote code and implemented systems) or neat (those that base AI on some formalism like first order predicate calculus). Out of necessity, researchers tend to focus on a particular aspect of intelligence to simulate. When this is done, the effect is to restrict the class of computations that are being considered. The goal is build pieces of intelligence.

R B. Abhyankar Emphasizing theory and implementation issues more than specific applications and Prolog programming techniques, Computing with Logic Logic Programming with Prolog (The Benjamin Cummings Publishing Company, Menlo Park, Calif., 1988, 535 pp., $27 95) by David Maier and David S. Warren, respected researchers in logic programming, is a superb book Offering an in-depth treatment of advanced topics, the book also includes the necessary background material on logic and automatic theorem proving, making it self-contained. The only real prerequisite is a first course in data structures, although it would be helpful if the reader has also had a first course in program translation. The book has a wealth of exercises and would make an excellent textbook for advanced undergraduate or graduate students in computer science; it is also appropriate for programmers interested in the implementation of Prolog The book presents the concepts of logic programming using theory presentation, implementation, and application of Proplog, Datalog, and Prolog, three logic programming languages of increasing complexity that are based on horn clause subsets of propositional, predicate, and functional logic, respectively This incremental approach, unique to this book, is effective in conveying a thorough understanding of the subject The book consists of 12 chapters grouped into three parts (Part 1 chapters 1 to 3, Part 2. chapters 4 to 6, and Part 3 chapters 7 to 12), an appendix, and an index The three parts, each dealing with one of these logic programming languages, are organized the same First, the authors informally present the language using examples; an interpreter is also presented. Then the formal syntax and semantics for the language and logic are presented, along with soundness and completeness results for the logic and the effects of various search strategies Next, they give optimization techniques for the interpreter Each chapter ends with exercises, brief comments regarding the material in the chapter, and a bibliography Chapter I presents top-down and bottom-up interpreters for Proplog Chapter 2 offers a good discussion of the related notions: negation as failure, closed-world assumption, minimal models, and stratified programs Chapter 3 considers clause indexing and lazy concatenation as optimization techniques for the Proplog interpreter in chapter 1 Chapter 4 explains the connection between Datalog and relational algebra. Chapter 5 contains a proof of Herbrand's theorem for predicate logic.

In the past, BDDs have significantly improved the performance of algorithms and enabled the solution of new classes of problems in areas such as formal verification and logic synthesis (see, for example, Burch et al. [1992]). Surprisingly, BDDs have only recently been introduced to implement the solution of planning problems (Cimatti et al. 1997). The goal of our project was to investigate whether BDDs might also help to increase the efficiency of algorithms solving problems in the field of reasoning about action and change. For a start, I have implemented the solution of fluent calculus planning problems restricted to deterministic actions and propositional fluents (Hölldobler and Störr 2000; Störr 2001). The idea of BDDs is similar to decision trees: A Boolean function is represented as a rooted acyclic-directed graph.

She argued that this ingrained conversational collaboration should be exploited to design successful natural language interfaces. Paul McKevitt, New Mexico State University, described a Wizard of Oz experiment in which it was found that particular sequences of speech act types have implications for the structure of the ensuing dialogue and can be correlated with certain aspects of the user, such as his experience in the domain. McKevitt contended that such empirical data, rather than subjective decision-making, should be the basis for constructing user models and argued for the development of automatic techniques for deriving the models. The second workshop was as successful as the first, with all agreeing that subsequent workshops should be held more frequently than at four year intervals. Since the general trend has been for researchers in different areas of user modeling to operate in isolation, such workshops are particularly important as a means of increasing cooperation and cross-fertilization of ideas among the subdisciplines.

Elliot Soloway is an Associate Professor of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor, MI. He directs the "Highly-Interactive Computing Environments" project located in the AI Lab. William J. Clancey is a Senior Research Scientist at the Institute for Research on Learning, an independent, not-for-profit organization. His current interests are relating AI programming to traditional scientific modeling, studying computer systems in the workplace, and reexamining the relation of cognitive science theories to the processes of human memory and learning. Kurt VanLehn is an associate professor in the computer science department and a senior scientist at the Learning Research and Development Center, both at the University of Pittsburgh.

Because it assumes so much previous knowledge, the book will not be useful to the casual reader. One would be at a disadvantage without a reasonable familiarity with predicate calculus and modal logic, AI planning formalisms, and the work of Perrault and Allen on interpreting speech acts (for example, Allen and Perrault [1980]; Perrault and Allen [1980]). Accordingly, the reader of this review should be warned that my point of view is that of a researcher (specifically, an academic researcher) rather than a system builder; your mileage might vary. No review of this book would be complete without some mention of the commentaries, critical pieces written by other workshop participants that follow groups of related papers. Each commentator did an excellent job.

About 70 participants from 10 countries attended the various talks and discussions in a particularly friendly and cooperative atmosphere. This workshop was sponsored by Simon Fraser University, the National Sciences and Engineering Research Council of Canada, the American Association for Artificial Intelligence, and the French Institut National de Recherche en Informatique et Automatique. The final proceedings will be published in April 1988 by North Holland. Before briefly introducing the main trends of the workshop, let me precisely define what is meant by natural language understanding and logic programming. In light of some of the talks and discussions, it turns out that this title applies to works where logic programming (in particular, Prolog, although there are a number of other logic-programming languages) is viewed as a convenient implementation framework and a clear (and sometimes simple) formal framework for describing linguistic phenomena.

In his new book Alternate Realities: Mathematical Models of Nature and Man (New York: John Wiley and Sons, 1989, 493 pages, $34.95), John L. Casti gives us an impressive, up-todate look at several areas of mathematics that are being applied to the study of biological and sociological systems. These areas, including cellular automata theory, catastrophe theory, nonlinear dynamics and chaos, game theory, and control theory, are finding use on the frontiers of scientific research. Although these areas and their applications are described in various other sources, both on the level of a scientist and a layperson, I know of no other book that brings them all together to show how they can be used in scientific research. However, this book suffers from being written for mathematical specialists and, therefore, limits the potential readership. An opportunity to educate more scientists in the use of mathematical models is regrettably missed.

How many AI people does it take to change a lightbulb? One to figure out how to describe lightbulb changing in first order logic. One to figure out how to describe lightbulb changing in second order logic. One to show the adequacy of FOL. One to show the inadequacy of FOL.