"Practice, and the performance improvement that it engenders, has long been a major topic in psychology. In this paper, both experimental and theoretical approaches are employed in an investigation of the mechanisms underlying this improvement On the experimental side, it is argued that a single law, the power law of practice, adequately describes all of the practice data. On the theoretical side, a model of practice rooted in modern cognitive psychology, the chunking theory of learning, is formulated. The paper consists of (1) the presentation of a set of empirical practice curves; (2) mathematical investigations into the nature of power law functions; (3) evaluations of the ability of three different classes of functions to adequately model the empirical curves; (4) a discussion of the existing models of practice; (5) a presentation of the chunking theory of learning." In J. R. Anderson (Ed.). Cognitive Skills and their Acquisition (pp. 1-55). Hillsdale, NJ: Erlbaum.

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.

A framework for inductive inference in logic is presented: a Model Inference Problem is defined, and it is shown that problems of machine learning and program synthesis from examples can be formulated naturally as model inference problems. A general, incremental inductive inference algorithm for solving model inference problems is developed. This algorithm is based on Popper's methodology of conjectures and refutations [II]. The algorithm can be shown to identify in the limit [3] any model in a family of complexity classes of models, is most powerful of its kind, and is flexible enough to have been successfully implemented for several concrete domains. The Model Inference System is a Prolog implementation of this algorithm, specialized to infer theories in Horn form.

Massachusetts Institute Technology Press

As a major scientific society, the AAAI has a responsibility for promoting its field as well as informing its members of the latest technical developments. Since the latter function is adequately performed by the several journals and conference proceedings already mentioned, the editorial committee chose to assign to AI Magazine the task of providing AAAI members and the public as well with a broader perspective on the research activities within AI. The approach we intend to take includes publishing informative expository and survey articles designed not so much for those working within a particular problem domain, but for those outside it who would like to gain a better understanding of the issues and methods currently being studied without having to cull all the technical literature.

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 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.

The Heuristic Programming Project of the Stanford University Computer Science Department is a laboratory of about fifty people whose main goals are to model the nature of scientific reasoning processes in various types of scientific problems and various areas of science and medicine; and to construct expert systems — programs that achieve high levels of performance on tasks that normally require significant human expertise for their solution.

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.

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.