Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1980.MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING.Includes bibliographical references.

Cambridge, Mass.: MIT Press. See also: An Overview of a Theory of Syntactic Recognition for Natural Language. MIT AIM-531. 1979 (http://dspace.mit.edu/handle/1721.1/6316).

The NLC system has grown out of an earlier series of studies on the "autoprogrammer" (Biermann[6]) and bears much resemblance to it. Program synthesis in both the current and the previous systems is based upon example calculations done by the user on displayed data structures. In the current system, the example is done in restricted English with all its power, which is a dramatic departure from the earlier approach, which simply involved pointing with a light pen. However, it is expected that many of the features from the autoprogrammer, such as "continue" and "automatic indexing", will transfer quite naturally into NLC. This paper emphasizes the natural language aspects of the system, while other reports deal with some of the additional automatic programming features. The relationship of NLC to other research in natural language processing is discussed in a later section. The next section presents an overview of NLC, after which subsequent sections discuss scanning, syntactic and semantic processing, and interpretation of commands in the "matrix computer". The next two sections discuss the processing of flow-of-control commands and the level of behavior achieved by the system. The final sections include a discussion of related research and conclusions.

The Hearsay-H speech-understanding system (SUS) developed at Carnegie-Mellon University recognizes connected speech in a 1000-word vocabulary with correct interpretations for 90 percent of test sentences. Its basic methodology involves the application of symbolic reasoning as an aid to signal processing. A marriage of general artificial intelligence techniques with specific acoustic and linguistic knowledge was needed to accomplish satisfactory speech-This research was supported chiefly by Defense Advanced Research Projects Agency contract F44620-73- C-0074 to Carnegie-Mellon University. In addition, support for the preparation of this paper was provided by USC/ISI, Rand, and the University of Massachusetts. We gratefully acknowledge their support. Views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official opinion or policy of DARPA, the U.S. government, or any other person or agency connected with them.

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.

The Stanford AI lab cart is a card-table sized mobile robot controlled remotely through a radio link, and equipped with a TV camera and transmitter. A computer has been programmed to drive the cart through cluttered indoor and outdoor spaces, gaining its knowledge about the world entirely from images broadcast by the onboard TV system.The cart deduces the three dimensional location of objects around it, and its own motion among them, by noting their apparent relative shifts in successive images obtained from the moving TV camera. It maintains a model of the location of the ground, and registers objects it has seen as potential obstacles if they are sufficiently above the surface, but not too high. It plans a path to a user-specified destination which avoids these obstructions. This plan is changed as the moving cart perceives new obstacles on its journey.The system is moderately reliable, but very slow. The cart moves about one meter every ten to fifteen minutes, in lurches. After rolling a meter, it stops, takes some pictures and thinks about them for a long time. Then it plans a new path, and executes a little of it, and pauses again.

This research was supported at SRI International by the Defense Advanced Research Projects Agency under contract N00039--79--C--0118 with the Naval Electronic Systems Command. The views and conclusions contained in this document are those of the author and should not be interpreted as representative of the official policies either expressed or implied of the Defense Advanced Research Projects Agency, or the U. S. Government. The author is grateful to Barbara Grosz, Gary Hendrix and Terry Winograd for comments on an earlier draft of this paper.

By contrast, or'ie ad0pting the cognitive stance makes conjectures about

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