Artificial Intelligence

Summary of PhD thesis, Computer Science Department, Stanford University, January, 1980,Stanford Rep. Nos. HPP-80-2, STAN-CS-80-784

Births are always interesting affairs. According to some, births are always traumatic — a shock to come from the womb to the new world. The birth we give witness to here is that of a new society, the American Association for Artificial Intelligence — AAAI. It has not seemed to me traumatic, but rather almost wholly benign. In a world where not much is benign at the moment, such an event is devoutly to be cherished. The proper topic for this initial message is talk about beginnings and circumstances, goals and aims, character and style. My premier duty as president of AAAI, it appears, will be to give a presidential address at the upcoming annual meeting. Specific precedents being absent, I need to give thought to what belongs in an AAAI presidential address. But one thing I already know: That talk should be devoted to our science, not our society. It should be substantive , not procedural. It should look inward at the state of what we know about intelligence and computers, not outward at our place in the larger society. It is in this message that earthly matters belong.

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.

This is an excerpt from the Handbook of Artificial Intelligence, a compendium of hundreds of articles about AI ideas, techniques, and programs being prepared at Stanford University by AI researchers and students from across the country. In addition to articles describing the specifics of various AI programming methods, the Handbook contains dozens of overview articles like this one, which attempt to give historical and scientific perspective to work in the different areas of AI research. This article is from the Handbook chapter on natural language understanding. Cross-references to other articles in the handbook have been removed-terms discussed in more detail elsewhere are italicized. Many people have contributed to this chapter, including especially Anne Gardner, James Davidson, and Terry Winograd. Avron Barr and Edward A. Feigenbaum are the Handbook's general editors.

Two premises, reflected in the title, underlie the perspective from which I will consider research in natural language processing in this article. First, progress on building computer systems that process natural languages in any meaningful sense (i.e., systems that interact reasonably with people in natural language) requires considering language as part of a larger communicative situation. Second, as the phrase “utterance and objective” suggests, regarding language as communication requires consideration of what is said literally, what is intended, and the relationship between the two.

The approach gives key emphasis to a succession of explicit descriptions at varying The MIT AI Laboratory has a long tradition of research in levels of visual processing, including the zero-crossing map, most aspects of Artificial Intelligence. Currently, the major foci the primal and 2'/2D sketches, and the so-called Spasar include computer vision, manipulation, learning, Englishlanguage 3D representation. Recent work has centered on directional understanding, VLSI design, expert engineering selectivity, evidence for a fifth, smaller channel for early problem solving, commonsense reasoning, computer processing, the Marr-Hildreth theory of edge detection, a architecture, distributed problem solving, models of human model of the retina, a computational theory of stereopsis and memory, programmer apprentices, and human education. Recently, Dr. Mike Brady has joined the Professor Berthold K. P. Horn and his students have studied Laboratory and has initiated a study of the psychology of intensively the image irradiance equation and its applications. The reflectance and albedo map representations have been introduced to make surface orientation, illumination geometry, and surface reflectivity explicit.

The representation language used in one domain is seldom biology, management-indeed in most of the world's workthe borrowed and adapted to another, because the facilities that daily tasks are those requiring symbolic reasoning with were assets for one task become limitations elsewhere. The computers that will act this reason, most such languages are built from scratch. The as "intelligent assistants" for these professionals must be goal of the RLL effort is to reduce the amount of time RLL contains a large library of "representational pieces," for example, the mode of inheritance used by the Examples link of the Units package, or the A-Kind-Of type of Artificial Intelligence Center slot used in the MIT Frames Representation Language, FRL. Menlo Park, CA 94025 amalgamation of pieces; RLL is responsible for meshing them together into a coherent and working whole. A more advanced Peter Hart, Director user can exploit RLL's mechanisms for designing new parts, Earl Sacerdoti, Associate Director for example, a new mode of inheritance, or a new type of Charles Untulis, Assistant Director format for a slot.

A classic introduction to artificial intelligence intended to bridge the gap between theory and practice, Principles of Artificial Intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval. Rather than focusing on the subject matter of the applications, the book is organized around general computational concepts involving the kinds of data structures used, the types of operations performed on the data structures, and the properties of the control strategies used. Palo Alto, California: Tioga.

Classic Ph.D. dissertations by Randall Davis and Douglas Lenat, reprinted together in one volume. New York: McGraw-Hill.