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Utterance and Objective: Issues in Natural Language Communication
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.
Research in Progress at the Massachusetts Institute of Technology Artificial Intelligence Laboratory
Horn, Berthold K. P., Marr, David, Hollerbach, John, Sussman, Gerald J., Winston, Patrick H., Davis, Randall, Minsky, Marvin L.
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.
Research In Progress at the Artificial Intelligence Center, SRI International
Hart, Peter, Sacerdoti, Earl, Untulis, Charles
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.
Principles of artificial intelligence
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.
Problem solving applied to natural language generation
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.
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.