Based on the experience in manufacturing production scheduling problems which the AI community has amassed over the last ten years, a workshop was held to provide a forum for discussion of the issues encountered in the design of AI-based scheduling systems. Several topics were addressed including : the relative virtues of expert system, deep method, and interactive approaches, the balance between predictive and reactive components in a scheduling system, the maintenance of convenient scheduling descriptions, the application of the ideas of chaos theory to scheduling, the state of the art in schedulers which learn, and the practicality and desirability of a set of benchmark scheduling problems. This article expands on these issues, abstracts the papers which were presented, and summarizes the lengthy discussions that took place.

Molecular biology is emerging as an important domain for artificial intelligence research. The advantages of biology for design and testing of AI systems include large amounts of available online data, significant (but incomplete) background knowledge, a wide variety of problems commensurate with AI technologies, clear standards of success, cooperative domain experts, non-military basic research support and percieved potential for practical (and profitable) applications. These considerations have motivated a growing group of researchers to pursue both basic and applied AI work in the domain. More than seventy-five researchers working on these problems gathered at Stanford for a AAAI sponsored symposium on the topic. This article provides a description of much of the work presented at the meeting, and fills in the basic biology background necessary to place it in context.

Models of design processes provide guidance in the development of knowledge-based systems for design. The basis for such models comes from research in design theory and methodology as well as problem solving in AI. Three models are presented: decomposition, case-based reasoning, and transformation. Each model provides a formalism for representing design knowledge and experience in distinct and complementary forms.

I appreciated very much the Spring 1990 issue of the AI Magazine on Robotic Assembly and Task Planning. It seems to me, however, that some good work that has been carried out on this subject in Europe during recent years has not been covered very much. Also commons on the low participation levels of women in the computer industry, suggestions for the inclusion of dissertation abstracts, comments on the Feldman article in the Fall 1990 issue, and a note about the discontinuance of plastic coverings on AI Magazine.

Models of design processes provide guidance in the development of knowledge-based systems for design. The basis for such models comes from research in design theory and methodology as well as problem solving in AI. Three models are presented: decomposition, case-based reasoning, and transformation. Each model provides a formalism for representing design knowledge and experience in distinct and complementary forms.

My Ph.D. dissertation describes an investigation of scientific reasoning from a computational perspective.

I concentrate on this class of design 1989) that lays out the relation problems in this article. An example of an implicit function mapping from behavior to structure), typically in many engineering devices is safety: For conducted by means of a search or exploration example, a subsystem's role might only be in the space of possible subassemblies explained as something that prevents the of components. This accent on assembly is in leakage of a potentially hazardous substance, fact the origin of the frequent suggestion that and this function might never be explicitly design is a synthetic task. Only a vanishingly design specifications will usually mention a small number of objects in this space constitute number of constraints. The distinction even satisficing, not to mention optimal, between functions and constraints is hard to solutions. What is needed to make design formally pin down; functions are constraints practical are strategies that radically shrink on the behavior or properties of the device. However, it is useful to distinguish functions Set against the view of design as a deliberative from other constraints because functions are problem-solving process is the view of the primary reason that the device is desired. Artistic creations and weigh more than..."), the process of making scientific theories are often said by their creators the artifact from its description (manufacturability to have occurred to them in this Even when a plausible solution itself (for example, "I want a design within a occurs in this way, the proposal still needs to week"), and so on.

Contrary to many prevailing approaches to knowledge acquisition, Laps, our expert-interviewing software, begins by soliciting cases from the expert, but it does not end there. Laps begins with a case in the form of a sample solution path elicited from the domain expert. This sample solution path is refined by a process called dechunking, which facilitates finding a model of the expert's reasoning process. Once these tables have been set up, the expert is able to produce row after row on his own until a complete rule base is built.

"Building Large Knowledge-Based Systems (Addison-Wesley, Reading, Massachusetts, 1990, 372 pages, $39.75, ISBN 0-201-51752-3) by Douglas B. Lenat and R. V. Guha is an interim report on the Microelectronic and Computer Technology Corpporation (MCC) Cyc project.

The second workshop in two years on the integration of knowledge-based systems with conventional computer techniques in agriculture and natural resource management (NRM) was held 18-19 August 1989 in Detroit, Michigan, in conjunction with the Tenth International Joint Conference on Artificial Intelligence. The workshop drew scientists from the United States and Canada, working in disciplines from engineering to entomology in universities, government, and industry. Twenty-two papers were presented at the workshop, after which participants were asked to discuss several key questions about the development, delivery, and use of knowledge-based systems in solving problems in agriculture and NRM.