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Information Technology
An outlook on truth maintenance
Truth maintenance systems have been used in several recent problem solving systems to record justifications for deduced assertions, to track down the assumptions which underlie contradictions when they arise, and to incrementally modify assertional data structures when assumptions are retracted. A TMS algorithm is described here that is substantially different from previous systems. This algorithm performs deduction in traditional propositional logic in such a way that the premise set from which deduction is being done can be easily manipulated. A novel approach is also taken to the role of a TMS in larger deductive systems. In this approach the TMS performs all propositional deduction in a uniform manner while the larger system is responsible for controlling the instantiation of universally quantified formulae and axiom schemas.
Natural language production as a process of decision making under constraint
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.
Speech recognition: Past, present, and future
In Lea, W, (Ed.). Trends in speech recognition. Englewood Cliffs, N.J.: Prentice-Hall, 1980, 39-89
Knowledge-based systems in artificial intelligence
Classic Ph.D. dissertations by Randall Davis and Douglas Lenat, reprinted together in one volume. New York: McGraw-Hill.
The HWIM Speech Understanding System
Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP '77, Vol 2, pp. 784-787, May 1977
Obstacle avoidance and navigation in the real world by a seeing robot rover
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.
Problem solving applied to natural language generation
Proceedings of the Association of Computational Linguistics, Philadelphia, 59-63