The Fairchild Laboratory for Artificial Intelligence Research (FLAIR) was inaugurated in October, 1980, with the purposes of introduction AI Technology into Fairchild Camera and Instrument Corporation, and of broadening the AI base of its parent company, Schlumberger Ltd. The charter of the laboratory includes basic and applied research in all AI disciplines. Currently, we have significant efforts underway in several areas of computational perception, knowledge representation and reasoning, and AI-related architectures. We also engage in various tool-building activities to support our research program. The current computational environment includes several large mainframes dedicated to AI research, a number of high-performance personal scientific machines, and extensive graphics capabilities.

To Date Gross margin, by activity: Rosenschein will complete McCarthy's term on the Council for

These are the voyages of the MIT Artificial Intelligence Laboratory, and these remarks may help to understand the context of this collection, though in many ways the memoranda speak quite clearly for themselves and my comments are not, in any case, to be regarded as history, for I have written them quite hastily, in much the same spirit of the memos themselves, when it was our strategy in those early days to be unscholarly: we tended to assume, for better or for worse, that everything we did was so likely to be new that there was little need for caution or for reviewing literature or for double -checking anything. As luck would have it, that almost always turned out true.

This paper explores the paradigm that heuristics are discovered by consulting simplified models of the problem domain. After describing the features of typical heuristics on some popular problems, we demonstrate that these heuristics can be obtained by the process of deleting constraints from the original problem and solving the relaxed problem which ensues. We then outline a scheme for generating such heuristics mechanically, which involves systematic refinement and deletion of constraints from the original problem specification until a semidecomposable model is identified. The solution to the latter constitutes a heuristic for the former.

This paper argues for the position that experimental human studies are relevant to most facets of AI research and that closer ties between AI and experimental psychology will enhance the development of booth the principles of artificial intelligence and their implementation in computers. Raising psychological assumptions from the level of ad hoc intuitions to the level of systematic empirical observation, in the long run, will improve the quality of AI research and help to integrate it with related studies in other disciplines.

THE COGNITION AND PROGRAMMING PROJECT (CAPP) to use such constructs effectively. Dr. Elliot Soloway, Assistant Professor; Dr. Kate which people bring to programming and that computing Ehrlich, Research Associate Lewis Johnson; Jeff Bonar; Valerie Abbott which arise due to cognztively poor programming language constructs. Work is currently in progress on the following projects: What do experts/novices know about programming. 'This project is currently being funded by NSF RISE, under grant'This project is currently being funded by NSF IST, under grant number TIIE AI MAGAZINE Winter/Spring 1083 17 then many individuals will not be able to acquire such languages; Soloway, E., Woolf, B., Rubin, E., Bonar, J. (1982) Overview moreover, it appears beneficial from a problem solving Vancouver, B.C. the empirical projects, we are actively engaged in building an Bonar, J., Ehrlich, K., Soloway, E., Rubin, E. (1982) Collecting AIbased tutoring system, PROUST, which can assist novice Behavioral Research Methods and Instrumentation, this system is to identify non-syntactic bugs in a student's Recent CAPP publications are listed below. What Do Novices Know About Programming?

In this article I wish to address some of the problems that confront AI. I am giving, no doubt, what amounts to no more than one man's opinion. It is my hope, in expressing these opinions, that the issues begin to be discussed in some public forum. I will attempt to start this debate by answering some questions about the field that have been posed to me over time. In some cases, what follows are questions that I have simply posed to myself.

IEEE Transactions on Computers, C-32 (9), 785–798

"One of the biggest problems in AT programming is the difficulty of specifying control. Meta-level architecture is a knowledge engineering approach to coping with this difficulty. The key feature of the architecture is a declarative control language that allows one to write partial specifications of program behavior. This flexibility facilitates incremental system dcvclopment and the integration of disparate architectures like demons, object-oriented programming, and controlled deduction. This paper presents the language, describes an appropriate, and cliscusses the issues of compiling. It illustrales the architecture with a variety of examples and reports some experience in using the architecture in building expert systems."Earlier: M. Genesereth and D.E. Smith. Meta-level Architecture. Memo HPP-81-6, Computer Science Department, Stanford University, 1981.In Proceedings of the AAAI, Washington, DC., August, 1983

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