GENERALIZATIONS BASED ON EXPLANATIONS Gerald DeJong Coordinated Science Laboratory University of Illinois 1101 West Springfield Avenue Urbana, IL 61801 This paper describes a new project at the University of Illinois in computer learning. The phenomenon under study is a kind of "insight learning" of procedural schemata. The system described here is designed to grasp some principle underlying a natural language input. Once acquired, the schema serves the same purpose as the other schemata in the system: it aids in processing future natural language inputs. The neutral term "schema" rather than "frame" (Minsky (1975), Charniak (1976)) or "script" (Schank A Abelson (1977)) is used to refer to knowledge chunks because a frame (which is used to describe static objects as well as progressions of world situations) is too general a notion, and the notion behind a script is-too specific.

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.

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.

The spatial and qualitative aspects of reasoning about motion through fret space arc studied through the construction of a program to perform such reasoning. An analog gcomctry rcprcscntation serves as a diagram, and descriptions of both the actual motion of a ball and envisioning are used in answering simple questions. WC ignore the exact shape of balls, motion after two balls collide, spin, motion in a third spatial dimension, air resistance, sliding motion, and all forces other than gravity. The initial description of a situation is a diagram containing a description of the surfaces and one or more bails, as in ligure 1. 1 Introduction People reason fluently about motion through space. For example, WC know that if two balls arc thrown into a well they might collide, but if one ball is always outside and the other always inside they cannot. The knowl"cdgc involved in this qualitative kind of reasoning seems to be simpler than formal mechanics and appears to be based on our expcricnce in the physical world.

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.

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After presenting the design considerations and their relationship to AI research, I will use our work with MYCIN to illustrate some of the ways in which we have attempted to respond to the acceptability criteria I have outlined.

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Frederick Hayes-Roth The RAND Corporation Using the concepts of stimulus and response frames of scheduled Knowledge source instantiations, competition among alternative responses, goals, and the desirability of a knowledge source instantiation, a general attentional control mechanism is developed. This general focusing mechanism facilitates the experimental evaluation of a variety of specific attentional control policies (such as best-first, bottom-up, and top-down search strategies) and allows the modular addition of specialized heuristics for the speech understanding task. Empirical results demonstrate the effectiveness of the focusing principles, and possible directions for future research are considered. INTRODUCTION The Hearsay-II (HSII) speech understanding system (Lesser, et al., 1974; Erman & Lesser, 1975; Lesser & Frman, 1977) is a complex, distributed-logic processing system. Inputs to the system are temporal sequences of sets of acoustic segments and associated hypothesized phonetic labels.

Our purpose in studying natural language understanding in conjunction with problem solving is to bring together the constraints of what formal representation can actually be obtained with the question of what knowledge is required in order to solve a wide range of problems in a semantically rich domain. We believe that these issues cannot sensibly be tackled in isolation. In practical terms we have had the benefits of an increased awareness of common problems in both areas and a realisation that some of our techniques are applicable to both the control of inference and the control of parsing. Early work on solving mathematical problems stated in natural language was done by Bobrow (STUDENT - (i]) and Chamiak (CARPS - [5]). However the rudimentary parsing and simple semantic structures used by Bobrow and Charniak are inadequate for any but the easiest problems. Our intention has been to build on B/RG Chris This work was supported by SRC grant number 94493 and an SRC research studentship for Mellish.