All Al programs are essentially reasoning programs. And, to the extent that they reason well about a problem area, all exhibit some expertise at problem solving. Programs that solve the Tower of Hanoi puzzle, for example, reason about the goal state and the initial state in order to find'expert-level' solutions. Unlike other programs, however, the claims about expert systems are related to questions of usefulness and understandability as well as performance. We can distinguish expert systems from other Al programs in the following respects: Utility Performance Transparency Designers of expert systems are motivated to build useful tools in addition to constructing programs that serve as vehicles for AI research.

A programming language based on sets; motivation and examples.

A programming language based on sets; motivation and examples.

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 inter-relations or inter-connections) 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. INTRODUCTION 1.1 Statement of the problem An input picture is read into a computer. We would like to analyze it. The input picture The input picture consists of a line drawing (black curved thin lines on white paper) containing familiar objects [figure 1(a)]; one could think of drawings in coloring books for children. The objects forming the picture should be drawn correctly and accurately: no intentional distortions, caricatures, or humanizations of animals (figure 2) will be allowed. Figure 2. Line drawings containing distortions, caricatures, comic strips, humanized animals, and so on, will not be accepted. Thus, it can be said that the class of input pictures we want to analyze is that found in coloring books, except distortions. We could think of a person looking at the input data [figures 1(a) or 1(b)] and saying: there is a straight line from point (30, 40) to point (67, --18.5), The input picture [figure 1(a)] is stored initially in the memory of the computer as a collection of black points (specified by their two-dimensional coordinates) closely spaced along each black line [figure 1(b)]. We will assume that: (a) The points are uniformly spaced along the lines of the drawing.

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'dead-pan' 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. The metric problem is considered de novo, and a metrical algorithm is described whose failures to generate Bach's notation are as illuminating as its successes. INTRODUCTION The performance of a piece of music involves both the performer and the listener in a problem of interpretation. The performer must discern and express musical relationships which are not fully explicit in the musical score, and the listener must appreciate relationships which are not explicit in the performance. How the performer should convey his interpretation of the piece is an aesthetic question of the utmost delicacy; but the converse process, that of listening to a piece and discerning its structure, is partly amenable to objective investigation. This is because European classical music is written in a notation which conveys to the performer a considerable amount of information about its structure, and this information can be reconstituted by the educated listener from even a mediocre performance. The'correct' annotation of the melody in question is, of course: This gap in musical theory is all the more glaring in view of the considerable effort which has been devoted to much more ambitious undertakings, such as programmed musical composition. We are cynical enough to believe that it is only the prevailing babel in contemporary classical music which saves most of these compositions from being treated with the derision which they merit, and that if any progress is to be made in this direction it will first be essential to formalize the most elementary facts about musical cOmpetence, such as those we have just mentioned.

The Genetics Counselor is a computer program, written in LISP, designed to handle problems of medical genetics counseling. It is an attempt to apply the methods of artificial intelligence research to medical diagnostic problems. The program attempts to map the data space of a family-tree structure into the hypothesis space of classical Mendelian genetics by use of a heuristic search. The input data are the family members along with their children (or parents), and phenotype. The program generates a family tree and searches for consanguinity.

In order to be able to view the situation objectively we feel that it would be useful to preface this with a historical review of the development of ideas in this twenty-year-old field. By considering the most important ideas and techniques that are employed in the (currently) best program available, we hope to convince the reader that progress has been very slow despite the multiplicity of programs (and their associated literature) which have appeared since 1950. HISTORICAL REVIEW The most important paper that has appeared on the subject of computer chess is one written by Claude Shannon in 1948 and published two years later (Shannon 1950). Shannon's paper does not describe an actual program, but offers many suggestions for those who are interested in writing one. In this respect Shannon's paper may be compared with one by Jack Good which was also full of sound ideas which could well be included in a successful chess program (Good 1967). Shannon stressed the importance of having a good evaluation function. The features which he considers necessary for inclusion in the evaluation function included material, mobility, five aspects of pawn-structure, four of the positions of pieces, and four of commitments of pieces, attacks and options. He appreciated that such an evaluation function should be used only in the middle-game, and that different principles applied to the opening and endgame phases of chess. He suggested that the values of the coefficients of the function should be determined by'some experimental procedure', and the fact that this statement has never been followed in practice is very surprising.

K.A.PATON 411 24 Centromere finding: some shape descriptors for small chromosome outlines.

K.A.PATON 411 24 Centromere finding: some shape descriptors for small chromosome outlines.

Editors' note The essay by Alan Turing, which we reproduce here, was written in September 1947, when the world's first stored-program digital computers, to a significant degree his own conceptual creation, were about to become operational. The paper was submitted in 1948 to the National Physical Laboratory, where Turing was then employed, as a report on his year's sabbatical leave which he had spent at Cambridge. During the same period Turing achieved his demonstration of the unsolvability of the word problem for semi-groups with cancellation. A condensed version is to appear in the Collected Works of A.M.Turing which is forthcoming under Dr Gandy's editorship. We also thank Mr Michael Woodger, who incidentally helped Turing finish it by drawing the original diagrams, for an unforgettable account of the furore created by Turing at N.P.L. with his prognostications of intelligent machinery: 'Turing is going to infest the countryside' some declared'with a robot which will live on twigs and scrap iron!' The anticipation of the notion of a sub-routine on page 21 and of the device of doing machine problem-solving via theorem-proving algorithms (p. Abstract The possible ways in which machinery might be made to show intelligent behaviour are discussed. The analogy with the human brain is used as a guiding principle. It is pointed out that the potentialities of the human intelligence can only be realized if suitable education is provided. The investigation mainly centres round an analogous teaching process applied to machines. The idea of an unorganized machine is defined, and it is suggested that the infant human cortex is of this nature. Simple examples of such machines are given, and their education by means of rewards and punishments is discussed. I propose to investigate the question as to whether it is possible for machinery to show intelligent behaviour. It is usually assumed without argument that it is not possible. Common catch phrases such as'acting like a machine', 'purely mechanical behaviour' reveal this common attitude. It is not difficult to see why such an attitude should have arisen. Some of the reasons are: (a) An unwillingness to admit the possibility that mankind can have any rivals in intellectual power. This occurs as much amongst intellectual people as amongst others: they have more to lose. Those who admit the possibility all agree that its realization would be very disagreeable.