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.

This article discusses building a computable design process model, which is a prerequisite for realizing intelligent computer-aided design systems. First, we introduce general design theory, from which a descriptive model of design processes is derived. Second, we show a cognitive design process model obtained by observing design processes using a protocol analysis method. In the computable model, a design process is regarded as an iterative logical process realized by abduction, deduction, and circumscription.

This article proposes connectionism as an alternative to classical cognitivism in understanding design. It also considers the difficulties encountered within a particular view of the role of explanations and typologies. Connectionism provides an alternative model that does not depend on the articulation of explanations and typologies.

"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 system, Hoist, performs fault diagnosis without the use of a repair expert or shallow rules. Its knowledge is coded directly from a structural specification of the Mark 45 lower hoist. In a mechanism like the lower hoist, the functional model must reason about forces, fluid pressures, and mechanical linkages; that is, it must reason about qualitative physics. Hypothetical reasoning, the process embodied in Hoist, has general utility in qualitative physics and reason maintenance.

In the last few years, interest has grown in exploring AI approaches to design problems, both because of the enormous potential impact on productivity of improved design tools and because of the interesting basic AI issues that these problems raise. In particular, a number of ship designers and AI researchers recently became interested in applying AI to the hydrodynamic design of ship hulls. A typical problem here is to design the shape of a ship's hull in response to desired hydrodynamic properties such as drag and stability, taking into consideration a variety of design constraints, such as total hull volume.

Many barriers exist today that prevent effective industrial exploitation of current and future AI research. These barriers can only be removed by people who are working at the scientific forefront in AI and know potential industrial needs. The Knowledge Processing Laboratory's research and development concentrates in the following areas: (1) natural language interfaces to knowledge-based systems and databases; (2) theoretical and experimental work on qualitative modeling and nonmonotonic reasoning for future knowledge-based systems; (3) application-specific language design, in particular, Prolog extensions; and (4) desi gn and analysis of neural networks. This article gives the reader an overview of the main topics currently being pursued in each of these areas.

Databases are at the heart of most real-world knowledge base systems. The management and effective use of these databases will be the limiting factors in our ability to build ever more complex AI systems. This article reports on a workshop that explored how databases and their associated technologies can best be used in the development of large AI applications.

The Advanced Architectures Project at Stanford University's Knowledge Systems Laboratory seeks to gain higher performance for expert system applications through the design of new, innovative software and hardware architectures. This research concentrates particularly on the use of parallel machines to gain speedup and the design of the software to exploit emergent paral-lel hardware architectures. This article describes the project and details its goals and the work performed in the pursuance of these goals. A brief description is given of each of the project components, and a complete bibliography appears of the publications produced for the project.

Text planning is one of the most rapidly growing subfields of language generation. Until the 1988 AAAI conference, no workshop has concentrated on text planning and its relationship to realiza-tion. This report is a summary of that workshop.