Commonsense reasoning about the physical world, as exemplified by "Iron sinks in water" or "If a ball is dropped it gains speed," will be indispensable in future programs. We argue that to make such predictions (namely, envisioning), programs should use abstract entities (such as the gravitational field), principles (such as the principle of superposition), and laws (such as the conservation of energy) of physics for representation and reasoning. These arguments are in accord with a recent study in physics instruction where expert problem solving is related to the construction of physical representations that contain fictitious, imagined entities such as forces and momenta (Larkin 1983). We give several examples showing the power of physical representations.

The notion of generic design, although it has been around for 25 years, is not often articulated; such is especially true within Newell and Simon's (1972) information-processing theory (IPT) framework. Design is merely lumped in with other forms of problem-solving activity. Intuitively, one feels there should be a level of description of the phenomenon that refines this broad classification by further distinguishing between design and nondesign problem solving. However, IPT does not facilitate such problem classification. This article makes a preliminary attempt to differentiate design problem solving from nondesign problem solving by identifying major invariants in the design problem space.

Failing to recognize this, significance. All interested readers The medium has misplaced the message understanding intelligence and cognition should be directed to his Ph.D. thesis [that should have appeared in Dr. Franck can be reached is merely irreverent, not irrelevant, per se " As readers can see, quite at the following address Dr. Bruno to AI All I can say is mea Thank you Columbus, OH 43210 culpa, and I hope this letter may help James R Slagle and Michael R. Wick to square things I read with great interest the excellent Information Processing. Engineering" by Ken Forbus in AI manuscript, I somehow managed to Professor Forbus's forceful changed the intended meaning rather Our recent article, entitled "A I personally as published reads: "The goal of these examples of our evaluation process thank him for writing such an eloquent gatherings has been to understand One of these examples involved the Various groups, especially for narrowly defined tasks (expert systems)." Naturally, I would not expect Prof. Forbus to enumerate IIICAD stands for "Intelligent, Integrated, and Interactive CAD" and was CWI is a research AAAI Membership Directory center in pure and applied mathematics An invaluable networking tool, this annual roster of AAAI members and computer science at Amsterdam. You've heard of this AAAI conference-it's the most distinguished Email: paulh@cwi.nl. TR An exciting opportunity to view the latest Al products, services, and CSR8744, CWI, Amsterdam research from industry and the academic community. These are the state-of-the-art research papers presented at the AAAl's A copy of this publication is included in conference Data Description Language for Coding registration; AAAI members not attending the conference may purchase Design Knowledge."

This book is the result of the author's research. The concepts it describes are illustrated in an expert recipe designer called CHEF. The book is well written, with many illustrations taken from CHEF dialogues and LISP code. It is not a textbook but would make a good reference for a college senior or first-year graduate class project. The author is somewhat repetitious, which is good if the reader is not familiar with the ideas of case-based reasoning.

Sometimes less ambiguous, are extremely complex.

In the restaurant script, notated as $RESTAURANT, the roles might directly to the United Press International Yale researchers explored intentionality include customer, waitress, and cook; news wire and could skim news One of the earliest programs to the props could be a menu, table, and stories in dozens of different domains, embody goals and plans within the silverware; the locations could be the and produce summaries in several languages. CD paradigm was Jim Meehan's bar, dining area, and kitchen; and the On the DEC-20 (which by TALESPIN, which made up stories events would include arriving, seating, 1978 had replaced the PDP-101, similar to the fables of Aesop.

This article presents an approach to a graduate-level course in expert, knowledge-based, problem-solving systems. The core of the course, and this article, is a set of questions called a profile, that can be used to characterize and compare each system studied.

Does the United States have a 51st state called Australia? A superficial look at the artificial intelligence (AI) research being done here could give that impression. A look beneath the surface, though, indicates some fundamental differences and reveals a dynamic and rapidly expanding AI community. General awareness of the Australian AI research community has been growing slowly for some time. AI was once considered a bit esoteric -- the domain of an almost lunatic fringe- but the large government -backed programs overseas, as well as an appreciation of the significance of AI products and potential impact on the community, have led to a reassessment of this image and to concerted attempt to discover how Australia is to contribute to the world AI research effort and hoe the country is to benefit from it. What we have seen as result is not an incremental creep of AI awareness in Australia but a quantum leap with significant industry and government support. The first systematic study of the Australian AI effort was undertaken by the Australian Department of Science (DOS) in 1986. The study took as its base the long-running research report Artificial Intelligence in Australia (AIIA), produced by John Debenham (1986). The picture that emerged is interesting. AI researchers are well qualified, undertaking research at the leading edge in their fields, and have significant potential to develop further. The results of this study were published by DOS in the Handbook of Research and Researchers in Artificial Intelligence in Australia (Department of Science1986). This article is based on key findings from the study and on additional information gained through meeting and talking with researchers and research groups.

Hand's book is well written and well researched. The author has taken great care in presenting previous work in detail and has quoted the erlier literature when applicable. Nevertheless, the book fails in two respects.

This article is a written version of an invited talk on artificial intelligence (AI) and the history of science that was presented at the Fifth National Conference on Artificial Intelligence (AAAI-86) in Philadelphia on 13 August 1986. Included is an expanded section on the concept of an abstraction in AI; this section responds to issues that were raised in the discussion which followed the oral presentation. The main point here is that the history of science can be used as a source for constructing abstract theory types to aid in solving recurring problem types. Two theory types that aid in forming hypotheses to solve adaptation problems are discussed: selection theories and instructive theories. Providing cases from which to construct theory types is one way in which to view the history of science as "complied hindsight" and might prove useful to those in AI concerned with scientific knowledge and reasoning.