We describe the analysis of visual scenes consisting of black on white drawings formed with curved lines, depicting familiar objects and forms: houses, trees, persons, and so on; for instance, drawings found in coloring books. The goal of such analysis is to recognize (by computer) such forms and shapes when present in the input scene; that is, to name (correctly) as many parts of the scene as possible: finger, hand, girl, dance, and so on. Complications occur because each input scene contains several such objects, partially occluding each other and in varying degrees of orientation, size, and so on. The analysis of these line drawings is an instance of'the context problem', which can be stated as'given that a set (a scene) is formed by components that locally (by their shape) are ambiguous, because each shape allows a component to have one of several possible values (a circle can be sun, ball, eye, hole) or meanings, can we make use of context information stated in the form of models, in order to single out for each component a value in such manner that the whole set (scene) is consistent or makes global sense?' Thus, shape drastically limits the values that a component could have, and further disambiguation is possible only by using global information (derived from several components and their interrelations or interconnections) under the assumption that the scene as a whole is meaningful. This paper proposes a way to solve'the context problem' in the paradigm of coloring book drawings. We have not implemented this approach; indeed, a purpose of this paper is to collect criticisms and suggestions.

We have attempted to discover formal rules for transcribing into musical notation the fugue subjects of the Well-Tempered Clavier, as this might be done by an amanuensis listening to a'deadpan' performance on the keyboard. In this endeavour two kinds of problem arise: what are the harmonic relations between the notes, and what are the metrical units into which they are grouped? The harmonic problem is that the number of keyboard semitones between two notes does not define-- their harmonic relation, and we further develop an earlier theory of such relations, arriving at an algorithm which assigns every fugue to the right key and correctly notates every accidental in its subject.

A general game-playing program must know the rules of the particular playing game. These rules are:(1) an algorithm indicating the winning state;(2) an algorithm enumerating legal moves. A move gives a set of changes from the present situation.There are two means of giving these rules:(1) We can write a subroutine which recognizes if we have won and another which enumerates legal moves. Such a subroutine is a black box giving to the calling program the answer: 'you win' or 'you do not win', or the list of legal moves. But it cannot know what is in that subroutine.(2) We can also define a language in which we describe the rules of a game. The program investigates the rules written with this language and finds some indications to improve its play. Artificial Intelligence and Heuristic Programming Edinburgh University Press

Session No. 2 Applications 59 CHALLENGE TO ARTIFICIAL INTELLIGENCE: PROGRAMMING PROBLEMS TO BE SOLVED Abstract J. E. Sammet IBM Corporation Cambridge, Mass. U. S. A. This paper is in the nature of a challenge to artificial intelligence experts. It suggests that the techniques of artificial intelligence should be applied to some realistic problems which exist in the programming and data processing fields. After a brief review of the little related existing work which has been done, the characteristics of programming problems which make them suitable for the application of artificial intelligence techniques are given. Specific illustrations of problems are provided under the broad categories of data structure and organization, program structure and organization, improvements and corrections of programs, and language. Descriptors artificial intelligence applications programming heuristic techniques I. INTRODUCTION It has been over 15 years since computers were first used for anything resembling "artificial intelligence". The pioneering work of Newell, Shaw, and Simon on proving theorems in the propositional calculus is so well known as not to need discussion for the people knowledgeable in the field of artificial intelligence.

Early programs were written to demonstrate that a particular task could be accomplished and could not periorm other tasks, even if quite similar, without being extensively rewritten. Generality unnecessary for the task at hand was sacrificed to keep the programs as *Currently on leave to The University of Jerusalem **Now at Computer Science Department, Rutgers University ***Is now at NIH, Bethesda, Maryland ****With Lockheed Palo Alto Research Labs //This research was supported by the Advanced research Projects Agency of the Department of Defense under Contract No. SD-183. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the Advanced Research Projects Agency of the U.S. Government. Bmall as possible so they would fit the core limitations of our computer. The main result of this research was the development of programs which could find and stack cubes, either sorting them by size (1), or ordering them by voice command (2).

This edge follower could easily find the outlines of white cubes on a black table, but was prone to error in less carefully controlled environments. Our studies of its inadequacies have stimulated the development of a more powerful edge follower, which overcomes most of the limitations of the old one. This program is currently the initial stage of visual processing in the Stanford hand-eye system (2). It has demonstrated an ability to track weak edges under adverse lighting conditions 2. HARDWARE The edge follower uses a standard vidicon television camera, modified to provide computer control of orientation (a pan-tilt head), focal length (a lens turret), color filter, focus, and target voltage. The lens iris is set manually. The pan-tilt head, lens turret, and focus motor *This research was supported by the Advanced research Projects Agency of the Department of Defense under Contract No. SD-183. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the Advanced Research Projects Agency of the U.S. Government.

The game of Go invites analysis. The rules seem few and simple, suggesting that the game may have helpful theorems. Tens of millions of people play and skill has developed over centuries to extraordinary levels. Thus, computer analysis can be tested against analysis by highly skilled human players. We study M × N boards, rather than the usual 19 × 19.

Kuhn (1962) has persuasively argued that science progresses by means of its paradigms--its models of the general nature of a research area--and that at the frontiers of research the primary quest is for a good paradigm. The small frontier outpost of language data processing has been characterized by an intensive seeking for a paradigm suitable to guide its researchers as they survey the complex topography of natural language structures. The earliest paradigm--one that led mechanical translators and early information retrievalists into a hopeless cul-de-sac--was that words (i.e.

Russian Mathematical Surveys, 25, 221-262

JOINDER OF CLAIMS, COUNTERCLAIMS, AND CROSS-COMPLAINTS: SUGGESTED REVISION OF THE CALIFORNIA PROVISIONS. Research in artificial intelligence, a branch of computer science, has illuminated our capacity to use computers to model human thought processes. In this Article we will argue that the time has come for serious interdisciplinary work between lawyers and computer scientists to explore the computer's potential in law. Interdisciplinary work between the lawyer and the computer scientist has floundered on the misconceptions that each has of the other's discipline. As a result, no one has yet attempted computer programs incorporating complex techniques of legal reasoning. Even efforts in legal information retrieval have been hampered by these misconceptions. In retrieval, lawyers have viewed the computer as, at most, a storehouse from which cases and statutes might be retrieved by skillfully designed indexing systems. But the lawyer rarely looks for, or even expects, clear answers. So far, the efforts in legal retrieval have given little consideration to the possibility that computers might operate on the legal data base the way a lawyer does. Yet the work in both fields law and computer science -,suggests that the computer modeling of legal reasoning would be a fruitful area for research. In this Article we speculate about the dimensions and possible directions of this research. Under the most promising of outcomes, interdisciplinary research could lead both to a greater understanding of the legal reasoning process and to the design of machine methods for performing parts of it. The prospect of using computers to model legal reasoning processes is likely to prompt a typically lawyer-like response: So what if we understand legal reasoning or legal argument formation better? Knowing more about the ways in which lawyers search and manipulate the legal data base might lead to improving the lawyer's skill at his work. We recognize the possibility that the work of many lawyers might actually involve little use of the legal data base for argument construction or dispute resolution.