Work on expert systems has received extensive attention recently, prompting growing interest in a range of environments. Much has been made of the basic concept and of the rule-based system approach typically used to construct the programs. Perhaps this is a good time then to review what we know, asses the current prospects, and suggest directions appropriate for the next steps of basic research. I'd like to do that today, and propose to do it by taking you on a journey of sorts, a metaphorical trip through the State of the Art of Expert Systems.

BBN's project in knowledge representation for natural language understanding is developing techniques for computer assistance to decision maker who is collecting information about and making choices in a complex situation. In particular, we are designing a system for natural language control of an intelligent graphics display. This system is intended for use in situation assessment and information management.

Work on expert systems has received extensive attention recently, prompting growing interest in a range of environments. Much has been made of the basic concept and of the rule-based system approach typically used to construct the programs. Perhaps this is a good time then to review what we know, asses the current prospects, and suggest directions appropriate for the next steps of basic research. I'd like to do that today, and propose to do it by taking you on a journey of sorts, a metaphorical trip through the State of the Art of Expert Systems. We'll wander about the landscape, ranging from the familiar territory of the Land of Accepted Wisdom, to the vast unknowns at the Frontiers of Knowledge. I guarantee we'll all return safely, so come along....

Research by members of the Department of Computer Science at Rutgers, and by their collaborators, is organized within the Laboratory for Computer Science research(LCSR). AI and AI-related applications are the major area of research within LCSR, with about forty people-faculty, staff and graduate students-currently involved in various aspects of AI research.

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.

The Nobel Prize in Economics was awarded to Herbert Simon in 1978. At Carnegie-Mellon University he holds the title of Professor of Computer Science and Psychology. These two facts together delineate the range and uniqueness of his contributions in creating meaningful interactions among fields that developed in isolation but that are all concerned with human decision-making and problem-solving processes. In particular, Simon has brought the insights of decision theory, organization theory (especially as it applies to the business firm), behavior modeling, cognitive psychology, and the study of artificial intelligence to bear on economic questions. This has led not only to new conceptual dimensions for theoretical constructions, but also to a new humanizing realism in economics, a way of taking into account and dealing with human behavior and interactions that lie at the root of all economic activity.

Othello is a recent addition to the collection of games that have been examined within artificial intelligence. Advances have been rapid, yielding programs that have reached the level of world-championship play. This article describes the current champion Othello program, iago. The work described here includes: (1) a task analysis of Othello; (2) the implementation of a program based on this analysis and state-of-the-art AI game-playing techniques; and (3) an evaluation of the program's performance through games played against other programs and comparisons with expert human play.