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.

Editor: We would like to thank you for your part in a recent result of special personal significance. The intensive mutual investigation inspired by our first meeting at a AAAI'87 reception has culminated in our marriage last week As we enjoy our honevmoon in Maui, we are grateful to you for helping us solve this previously open problem in parallel search Sincerely, lack Mostow and fanet Tyroler Mostow Rutgers University, Hill Center Busch Campus, Computer Science Dept New Brunswick, NJ 08903 Editor: The medium has misplaced the message [that should have appeared in Winter 1988, p. 41 that I am now an assistant professor at the Ohio State University's LAIR, where connectionism is merely irreverent, not irrelevant, to AI fordan Pollack Computer and Information Science Dept, The Ohio State University Columbus, OH 43210 Editor: The Winter 1988 issue of AI Magazine carried a report I coauthored reporting on the June, 1987 Workshop on Theoretical Issues in Conceptual Information Processing. Despite the many chances I had to fix the manuscript, I somehow managed to miss an error that crept in and changed the intended meaning rather drastically in the section "From the Workshop Chair," by B. Chandrasekaran. A sentence in that section as published reads: "The goal of these gatherings has been to understand intelligence and cognition as feasible computations as they apply to the construction of performance programs for narrowly defined tasks (expert systems)." The sentence as originally written by Chandrasekaran read: "The goal of these investigations has been understanding intelligence and cognition as feasible computations, as opposed to the construction of performance programs for narrowly defined tasks (expert systems) or formalization per se " As readers can see, quite a difference in meaning His goal was to characterize how the shared goals of the participants in the TICIP series differ from some other groups of researchers in AI.

The lack of time affects both preflight training for, and in-flight operation of, the experiment. This difficulty with time is currently true with the Space Shuttle Program and will persist with the advent of Space Station Freedom operations. Another key factor in space experimentation is the use of fixed experiment protocols. This major constraint severely limits the ability of an earthbound scientist to change the course of an experiment even when the data and current situation clearly indicate that it would be scientifically more valuable to do so. The goal is to help the astronaut become a scientific collaborator with the ground-based principal investigator who designed the experiment.

Having convenient access to expert knowledge is important. In the past, we have seen users reinvent solutions because they did not have access to previous experience on the same fault. This lack of available information has led to wasted resources and, in some cases, has generated responses to the fleet that were not accurate enough. The development began in fiscal year 1992, and the area between the solid and dotted lines approximates the cost for development. The peak in fiscal year 1994 represents the end of the operational evaluation and the beginning of production operation.

It is organized around projects as "a history and assessment of efforts to mechanise processes of translating" (p.18). It is complete, discussing basically every project in the world since machine translation's first glimmerings 40 years ago Projects are grouped by time frame, nation, or approach. The organization is, of course, somewhat arbitrary, but it is supplemented by cross-references and summary tables of projects and systems. Hutchins not only presents the theories, algorithms, and designs but also the history, goals, assumptions, and constraints of each project. There are many sample outputs and fair evaluations of the contributions and shortcomings of each approach.

The Spring Symposium on Knowledge-based Environments for Teaching and Learning focused on the use of technology to facilitate learning, training, teaching, counseling, coaxing and coaching. Sixty participants from academia and industry assessed progress made to date and speculated on new tools for building second generation systems. Selection of topics and participants was motivated by a desire for ideological breadth and depth. Panel leaders included William J. Clancey and Alan Lesgold (researchers of realworld systems); Kurt VanLehn (champion of cognitive models); Beverly Park Woolf (defender of discourse systems); Elliot Soloway (advocate for alternative environments); and Sarah Douglas (spokesperson for supportive systems). Researchers have moved away from building omniscient tutors capable of detecting all possible errors and misconceptions.

To give an integrated view of current research issues in this field, we organized this article along thematic lines, unifying the reports of the two separate meetings. Our report focuses on the trends that we think will be important in the near future in this field. The 1997 edition of EUROVAV was already the fourth time that the symposium was held. It was chaired by Jan Vanthienen (University of Leuven, Belgium) and Frank van Harmelen (Vrije Universiteit, Amsterdam) and held in the beautiful city of Leuven, Belgium. With 25 submissions (of which 16 were accepted) and 35 attendants, EUROVAV'97 was roughly the same size as other recent meetings.

The question comes from an old joke about a Boston politician talking to voters in his district. "Will you vote for me? I gave your father a job at city hall, I found jobs for your wife, your sons, and your daughter. Last year I directed a million dollars worth of business to your company. And I got the city to repair your street."

Leibniz's (1984) An Introduction to a Secret Encyclopedia includes the following marginal note: Principle of Physical Certainty: Everything which men have experienced always and in many ways will still happen: for example that iron sinks in water (Leibniz 1984). In our daily lives, we routinely use this principle. Thus, we know that we can pull with a string but not push with it; that a flower pot dropped from our balcony falls to the ground and breaks; that when we place a container of water on fire, water might boil after a while and overflow the container. The origin of such knowledge is a matter of constant debate. It is clear that we learn a great deal about the physical world as we grow up.