Artificial intelligence is that part of computer science that concerns itself with the concepts and methods of symbolic inference and symbolic representation of knowledge. Its point of departure -- it's most fundamental concept -- is what Newell and Simon called (in their Turing Award Lecture) "the physical symbol system." But within the last fifteen years, it has concerned itself also with signals -- with the interpretation or understanding of signal data. AI researchers have discussed "signal-to symbol transformations," and their programs have shown how appropriate use of symbolic manipulations can be of great use in making signal processing more effective and efficient. Indeed, the programs for signal understanding have been fruitful, powerful, and among the most widely recognized of AI's achievements.

This paper presents the view that artificial intelligence (AI) is primarily concerned with propositional languages for representing knowledge and with techniques for manipulating these representations. In this respect, AI is analogous to applied in a variety of other subject areas. Typically, AI research (or should be) more concerned with the general form and properties of representational languages and methods than it is with the context being described by these languages. Notable exceptions involve "commonsense" knowledge about the everyday would ( no other specialty claims this subject area as its own ), and metaknowledge (or knowledge about the properties itself). In these areas AI is concerned with content as well as form. We also observe that the technology that seems to underly peripheral sensory and motor activities (analogous to low-level animal or human vision and muscle control) seems to be quite different from the technology that seems to underly cognitive reasoning and problem solving. Some definitions of AI would include peripheral as well as cognitive processes; here we argue against including the peripheral processes.

When I returned to Stanford last summer after a two-year leave of absence, serving as a program manager at the Defense Advanced Projects Agency, I was frequently asked about that experience. It was superb experience, for many reasons. As a program manager I had near-perfect vantage point from which to view the entire field of Artificial Intelligence. Not only did I become better acquainted with the most creative and active people in the field, I was also personally kept up to date on their latest research. ARPA is not just a place to go to provide a public service, but is really a central node in the research network for collecting and integrating results and disseminating them to the broader community: government, industry and the public at large. Moreover, it was my responsibility to identify new avenues of research and/or applications of research, coupled with the resources (limited, but real) to make these new activities happen -- a unique opportunity.

Our program in AI is part of the larger departmental focal area of cybernetics which integrates both AI and brain theory (BT). Our research also draws upon a new and expanding interdepartmental program in cognitive science that brings together researchers in cybernetics, linguistics, philosophy, and psychology. This interdisciplinary approach to AI has already led to a number of fruitful collaborations in the areas of cooperative computation, learning, natural language parsing, and vision.

Summary of the Executive Council meeting held 26 August 1981 in Vancouver, British Columbia.

Amidst the beautiful foliage in Jackson, N.H., the each session circulated a position paper to the group, Second KL-ONE Workshop was held over a five-day raising the questions he wanted to see addressed at the period this past October. Not only did "KloneTalk" (a version of KL-ONE implemented in we have a general conference session, wherein people SmaiiTa k at Xerox PARC -- this inchrded a videotaped could report on activities at their own institutions, discuss demonstration of the system's interface), prototypes in issues of general interest, etc., we also had a knowledge representation, translation of INTERLISP two-and-a-half day working research session. KL-ONE to FranzLisp, a calculus of Structural The technieai discussion part of the Workshop Descriptions, and the KL-ONE Classifier, not to mention several others. We also had the larger group break up preceded the general conference, so that we could report into smaller working groups to consider inference in on findings to the larger group of participants (forty-six KL-ONE, representing beliefs, some KL-ONE practice this year, from twenty-one institutions). Also, we planned to cover only a small extensive Proceedings of the Workshop.

Consider a class of computing problem for which all bananas is left as an exercise for the reader, or the sufficiently short programs are too slow and all sufficiently monkey. When it has been possible to couple causal models problems of this kind were left strictly alone for the first with various kinds and combinations of search, twenty-years or so of the computing era. There were two mathematical programming and analytic methods, then good reasons. First, the above definition rules out both evaluation of t has been taken as the basis for "high road" the algorithmic and the database type of solution. In "low road" representations Second, in a pinch, a human expert could usually be s may be represented directly in machine memory as a set found who was able at least to compute acceptable A recent pattern-directed allocation, inventory optimisation, or whatever large heuristic model used for industrial monitoring and control combinatorial domain might happen to be involved.

Cambridge University Press

ACM Computing Surveys, 14(l), March

Robot Motion is included in theMIT Press Artificial Intelligence Series.