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) informationprocessing 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.

The first Artificial Intelligence (AI) and simulation workshop was held during the National Conference on Artificial Intelligence (AAAI-86) on 11 August 1986 at Wharton Hall, the University of Pennsylvania It was attended by over forty participants from academic, government, and industrial institutions. It included paper presentations, informal discussions, and a panel summary of AI and simulation applications in the areas of: (1) State of the art and future directions in AI and simulation (Authur Gerstenfeld, Worcester Polytechnic Institute); (2) AI problem solving using simulation (Y.V. Ramana Reddy, University of West Virginia); (3) Knowledge representation issues related to simulation (Marilyn Stelzner, Intellicorp); (4) Engineering issues related to AI and simulation (Dick Modjeski, US Army Concepts Analysis Agency). Individual presentations given in each of the above areas of the workshop are published in a technical report distributed by the Defense Technical Information Center (DTIC Number AD-Al74 053). A copy of the report can be obtained by calling DTIC at (202) 274-6847/6874. The intersection of AI and simulation may offer a unique application of computer science that may be of use to both fields.

The t.estbed simulates a class of a distributed knowledge-based THERE ARE TWO MAJOR T IEMES of this article. First, WC introduce readers to the emerging subdiscipline of AI called Dzstrrbuted Problem Solving, and more specifically the authors' research on Functionally Accurate, Cooperative systems Second, we discuss the st,ructure of tools that allow more thorough experimentation than has typically been performed in AI research An examplr of such a tool, the Distributed Vehicle Monitoring Testbed, will bc presented. The testbed simulates a class of dist,ributed knowledge-based problem solving systems operating on an abstracted version of a vehicle monitoring task. This presentation emphasizes how the t,estbed is structured to facilit,ate the study of a wide range of issues faced in t,he design of distributed problem solving networks. Distribut,ed Problem Solving (also called Distributed Al) combines the research interests of the fields of AI and Distributed Processing (Chandrasekaran 1981; Davis 1980, 1982; Fehling & Erman 1983).

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.

Mind, that is based on a new television series shown on BBC, but not yet in America. The book is a very well edited transcription of fifteen interviews with psychologists, anthropologists, and sociologists, including such n,otables as George Miller, Jerome Bruner, and Rom Harre. The contributors probably familiar to most AI researchers are Daniel Dennett and Jerome Fodor, as well as two contributors well-known for their writing on art and perception, Ernst Gombrich and Richard Gregory. The interviews are uniformly intelligent, original, and stimulating. As summaries of basic arguments about mental models, perception, and ethical questions of mental problems, you can't do better than this collection.

For several decades, there has been another face to the field, a technological one that provides tools for solving practical problems in various domains. Of these domains, engineering and medicine have had the closest interaction with AI. AI ideas in representation and reasoning have been especially relevant to diagnosis and corrective action planning in both domains, and in engineering, design has been another area of very fruitful collaboration. These disciplines have not been mere consumers of AI ideas and technology, however. They have had a deep effect on the theoretical side of AI. Complex real-world reasoning tasks, such as those in engineering and medicine, bring out the inadequacy of purely theoretical conceptions about the nature of intelligence. The enormous amounts and types of knowledge often required for carrying out practical reasoning, and the variety of inference techniques that are displayed by practitioners, and the need to arrive at conclusions rapidly--these ...

Our group's work in medical decision making has led us to formulate a framework for expert system design, in particular about how the domain knowledge may be decomposed into substructures We propose that there exist different problem-solving types, i e, uses of knowledge, and corresponding to each is a separate substructure specializing in that1 type of problem-solving Each substructure is in turn further decomposed into a hierarchy of specialists which differ from each other not in the type of problem-solving, but in the conceptual content of their knowledge; e g, one of them may specialize in "heart disease," while another may do so in "liver," though both of them are doing the same type of problem-solving Thus ultimately all the knowledge in the system is distributed among problem-solvers which know how to use that knowledge This is in contrast to the currently dominant expert system paradigm which proposes a common knowledge base accessed by knowledge-free problem-solvers of various kinds In our framework there is no distinction between knowledge bases and problem-solvers: each knowledge source is a problem-solver We have so far had occasion to deal with three generic problem-solving types in expert clinical reasoning: diagnosis (classification), data retrieval and organization, and reasoning about consequences of actions In a novice, these expert structures are often incomplete, and other knowledge structures and learning processes are needed to construct and complete them This is a revised and extended version of an invited talk entitled, "Decomposition of Domain Knowledge Into Knowledge Sources: The MDX Approach," delivered at the IV National Conference of the Canadian Society for Computational Studies of Intelligence, May 17-19, 1982, Saskatchewan For the past few years our research group has been investigating the issues of problem-solving as well as knowledge organization and representation in medical decision making. In parallel with this investigation we have also been building and extending a cluster of systems for various aspects of medical reasoning. MDX, which is a diagnostic system, i.e., its role is to arrive RADEX is a Though in a sense RADEX and PATREC can both be viewed as "intelligent" data base specialists, RADEX has some additional features of interest due to the perceptual nature of some of its knowledge. However, for the purpose of this paper, it is not necessary to go into RADEX in much detail, and we can view PATREC as prototypical of this class of auxiliary systems. Our aim in this paper is to outline a point of view about how a domain gets naturally decomposed into substructures each of which specializes in one type of problem-solving.

This report is a review of the First National Conference on Knowledge Representation and Inference in Sanskrit, Bangalore, India, 20 through 22 December, 1986 The conference was inspired by an article that appeared in the Spring 1985 issue of AI Magazine--"Knowledge Representation in Sanskrit and Artificial Intelligence." A working group has been created to pursue the goals of the conference and to possibly arrange another conference for 1987 and 1988 This conference is analogous to the consultation of philosophers and cognitive psychologists by computer scientists in the beginnings of AI. Western psychology and philosophy is quite different from the Indo-Aryan tradition: the former has its basis in Aristotelian logic and the scientific method, whereas the latter is also based on introspection and internal experience Nevertheless, both these schools have converged in the analysis of natural language and the extraction of the semantic message from a text.The purpose of AI in this context is to derive a "method" for natural language understanding; the purpose for the Sanskrit scholars was to understand the nature of language and thought in and of itself. Hence, for the Sanskrit scholars, the actual methodology was implicit; it was not the focus. The purpose of the conference was to extract this hidden "algorithm" of automatic semantic parsing from the Sanskrit pandits.

In the past twenty years, much time, effort, and money has been expended on designing an unambiguous representation of natural languages to make them accessible to computer processing These efforts have centered around creating schemata designed to parallel logical relations with relations expressed by the syntax and semantics of natural languages, which are clearly cumbersome and ambiguous in their function as vehicles for the transmission of logical data. Understandably, there is a widespread belief that natural languages arc unsuitable for the transmission of many ideas that artificial languages can render with great precision and mathematical rigor. But this dichotomy, which has served as a premise underlying much work in the areas of linguistics and artificial intelligence, is a false one There is at least one language, Sanskrit, which for the duration of almost 1000 years was a living spoken language with a considerable literature of its own Besides works of literary value, there was a long philosophical and grammatical tradition that has continued to exist with undiminished vigor until the present century. Among the accomplishments of the grammarians can be reckoned a method for paraphrasing Sanskrit in a manner that is identical not only in essence but in form with current work in Artificial Intelligence This article demonstrates that a natural language can serve as an artificial language also, and that much work in AI has been reinventing a wheel millenia old First, a typical Knowledge Representation Scheme (using Semantic Nets) will be laid out, followed by an outline of the method used by the ancient Indian Grammarians to analyze sentences unambiguously. Finally, the clear parallelism between the two will be demonstrated, and the theoretical implications of this equivalence will be given.

In terms of basic research, our current focus is the development, of broadly applicable techniques for description and matching of structure in sensory data. Such techniques appear to lmderlie virtually every aspect of early and intermediate vision, such as edge and region finding, perceptual organization and grouping, and the recovery of 3-D shape from contour, texture, stereo and motion They appear to be equally important in other sensory domains, such as audition (e g, for describing the structure in spectrograms.) In particular, we are dealing with the problem of grey-level inspection, and are constructing a vision workbench to allow rapid experimentation with alternative techniques Finally, WC are examining a variety of special-purpose architectures for image processing. These range from a SUN (MC68000-based) workstation, augment,cd with high-speed pipelined VLSI components, to a massively parallel architerture involving a thousand processors and a novel interconnection network. Knowledge Representation Contact: Ronald J. Brachman Having had experience with knowledge representation syst,ems designed to support "common sense" reasoning, we are developing and implementing a new framework for representation and reasoning in arcas requiring "expertise."