The Stanford Artificial Intelligence Project, later known as the Stanford AI Lab or SAIL, was created by Prof. John McCarthy shortly after his arrival at Stanford on 1962. As a faculty member in the Computer Science Division of the Mathematics Department, McCarthy began supervising research in artificial intelligence and timesharing systems with a few students. From this small start, McCarthy built a large and active research organization involving many other faculty and research projects as well as his own. There is no single theme to the SAIL memos. They cannot be easily categorized because they show a diversity of interests, resulting from the diversity of investigators and projects. Nevertheless, there are some important dimensions to the research that took place in the AI Lab that will try to put in historical context in this brief introduction.

This report covers two main AI areas: natural language processing and expert systems. The discussion of each area includes an assessment of the state-of-the-art, an enumeration of problems areas and opportunities, recommendations for the next 5-10 years, and an assessment of the resources required to carry them out. A discussion of possible university-industry-government cooperative efforts is also included.

Machine learning has always been an integral part of artificial intelligence, and its methodology has evolved in concert with the major concerns of the field. In response to the difficulties of encoding ever-increasing volumes of knowledge in modern AI systems, many researchers have recently turned their attention to machine learning as a means to overcome the knowledge acquisition bottleneck. This article presents a taxonomic analysis of machine learning organized primarily by learning strategies and secondarily by knowledge representation and application areas. A historical survey outlining the development of various approaches to machine learning is presented from early neural networks to present knowledge-intensive techniques.

The third national conference promotes research in the field of AI by bringing together individuals from government, industry, and academia and by providing a published record of the conference as proceedings.

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?

AI research at CMU is closely integrated with other activities in the Computer Science Department, and to a major degree with ongoing research in the Psychology Department. Although there are over 50 faculty, staff and graduate students involved in various aspects of AI research, there is no administratively (or physically) separate AI laboratory. To underscore the interdisciplinary nature of our AI research, a significant fraction of the projects listed below are joint ventures between computer science and psychology.

"Practice, and the performance improvement that it engenders, has long been a major topic in psychology. In this paper, both experimental and theoretical approaches are employed in an investigation of the mechanisms underlying this improvement On the experimental side, it is argued that a single law, the power law of practice, adequately describes all of the practice data. On the theoretical side, a model of practice rooted in modern cognitive psychology, the chunking theory of learning, is formulated. The paper consists of (1) the presentation of a set of empirical practice curves; (2) mathematical investigations into the nature of power law functions; (3) evaluations of the ability of three different classes of functions to adequately model the empirical curves; (4) a discussion of the existing models of practice; (5) a presentation of the chunking theory of learning." In J. R. Anderson (Ed.). Cognitive Skills and their Acquisition (pp. 1-55). Hillsdale, NJ: Erlbaum.

Over the past two years we have started a program of On the theoretical side, Professor Randall Davis has research into the development of VLSI systems. They have introduced a descriptive formalism called OMEGA, which contributes to many of the issues of Traditional automated synthesis techniques for circuit current concern in knowlege representation, and they have design are restricted to small classes of circuit functions for applied it to describe the various structured entities such as which mathematical methods exist. Sussman and his group have developed computer-aided design tools that can be of much broader assistance. Guy L. Steele developed a language to support such programming, Johan de Kleer studied causal and Professor Marvin Minsky has worked on a theory of human teleological reasoning in the recognition of circuit function thinking, which likens the mind to a society of agents and from schematics, and Howie Shrobe has worked on constraint attempts to combine a number of insights from satisfaction and the development of an interactive knowledgebased psychoanalytic, developmental, and cognitive theories of system for substantially supporting VLSI design. Further work by Richard Greenblatt and Dr. Lucia Doyle has studied belief revision via truth maintenance and Vaina develops the idea of thread memory.

Computer-based tutoring/coaching systems have the promise of enhancing the educational value of gaming environments by guiding a student's discovery learning. This paper provides an in-depth view of (i) the philosophy behind such systems, (ii) the kinds of diagnostic modeling strategies required to infer a student's shortcomings from observing his behavior and (iii) the range of explicit tutorial strategies needed for directing the Tutor to say the right thing at the right time. Examples of these issues are drawn for a computer-based coaching system for a simple game-How the West was Won. Our intention in writing this paper is to make explicit the vast amounts of tutorial knowledge required to construct a coaching system that is robust, friendly and intelligent enough to survive in home or classroom use. During the past three years, we have witnessed how subtle the computer-based coaching problem really is.

This chapter focuses on ACT system that embodies the extremely powerful thesis that a single set of learning processes underlies the whole gamut of human learning—from children learning their first language by hearing examples of adult speech to adults learning to program a computer by reading textbook instructions. The computer simulation is called ACT. The ACT theory describes its application to research on abstraction of schemas. In ACT, knowledge is divided into two categories: declarative and procedural. The declarative knowledge is represented in a propositional network similar to semantic network representations.