This paper echoes, from a philosophical standpoint, the claim of McCarthy and Hayes that Philosophy and Artificial Intelligence have important relations. Philosophical problems about the use of “intuition” in reasoning are related, via a concept of anlogical representation, to problems in the simulation of perception, problem-solving and the generation of useful sets of possibilities in considering how to act. The requirements for intelligent decision-making proposed by McCarthy and Hayes are criticised as too narrow, and more general requirements are suggested instead.See also: Artificial Intelligence, Volume 2, Issues 3–4, Winter 1971, Pages 209–225In IJCAI 1971: INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE.. Revised paper in Artificial Intelligence 2:209- 225

This paper describes the programming of a computer controlled arm. The programming is divided logically into planning and execution Communication between planning and execution is by a data fil e which specifies the arm trajectory with reapect to time, and actions that the arm should perform. The servo program which moves the arm along the trajectory is based on Legrangian mechanics and takes into account coupling between links, and the variation of inertial loading with change of arm configuration.In IJCAI-71: INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE. British Computer Society, London.

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

One of the central principles upon which intelligent devices seem to operate is that of maintaining internal models of their external environments. How difficult this is, depends upon both the complexity of the model and its method of representation. In particular, it is usually easy when the problem is posed in the classical heuristic search paradigm, and the data structures used to represent static configurations of the puzzle are relatively unproblematic (arrays, lists, and so on). The lack of side-effects reflects the simplicity of the physics which such models embody. This limitation to elementary forms of interaction is not, of course, intrinsic to the heuristic search method; but when more complex models are constructed it becomes less trivial to pursue the consequences of performing an action. This approach is more general than the heuristic search method (but the latter -- when it has sufficient expressive power -- wins at present by its computational advantage). Assertions mentioning several different situations can then be used to describe dynamical laws which move us from one situation to another. But in some ways the resulting sharp separations between states of affairs are an embarrassment. For if we distinguish two situations s1 and s2, then from the fact, if such it be, that a predicate p is true of Si, nothing whatever follows concerning s2. And this is true even when s2 is directly associated with sl. Say s2 results from s1 by the performance of some action: s2 do (a, si) then no matter how remote -- speaking intuitively -- the connection between the property p and the action a, it still does not follow that p is true of s2. If we want it to so follow we must state this explicitly. Now, unfortunately, there are innumerable facts which might remain unchanged when actions are performed. So instead of writing a law of motion' in the form A(s) B(do(a, s)) where A and B are fairly short expressions, we are apparently obliged to list systematically all conceivable facts which are not changed. So that the law looks more like (Ci(s)& Ci(do(a, s))& & C„(do(a, s))&B(do(a, s)) for some very large n. This works for small problems (such as the familiar hungry anthropoid), but these are usually better formalized in the heuristic search paradigm anyway.

To every 3-dimensional scene there correspond as many 2-dimensional pictures as there are possible vantage points for the camera. It is, however, possible to construct pictures for which there is no corresponding scene containing physically -realizable objects. Pictures of such'impossible objects' can be useful in giving insight into the constraints or grammatical rules associated with the'language' of pictures, just as nonsense sentences can be useful in illustrating the rules of other languages. Impossible objects have been used by psychologists (Penrose and Penrose 1958) to create visual illusions which successfully challenge the ability of our perceptual systems to synthesize a 3-dimensional world from 2-dimensional information. The incompatibilities among the various portions of pictures of these objects are a novel way of testing our picture analysis procedures. The purpose of this paper is to demonstrate some possible decision procedures and to test them on pictures of both possible and impossible objects.

In this paper, we develop the algorithm, given in Plotkin (1970), for findingthe least generalization of two clauses, into a theory of inductive generalization.The types of hypothesis which can be formed are very simple. They allhave the form: (x)Px --> Qx.We have been guided by ideas from the philosophy of science, followingBuchanan (1966). There is no search for infallible methods of generatingtrue hypotheses. Instead we define (in terms of first-order predicate calculus)the notions of data and evidence for the data. Next, some formal criteria areset up for a sentence to be a descriptive hypothesis which is a good explanationof the data, given the evidence. We can then look for the best such hypothesis.Machine Intelligence 6

"In this paper we describe work on the recognition by computer of objects viewed by a TV camera. We have written a program which will recognize a range of objects including a cup, a wedge, a hammer, a pencil, and a pair of spectacles. A visual image, represented by a 64.× 64 array of light levels, is first partitioned into connected regions. These regions are chosen to have well-defined edges.Having chosen the regions, the program then computes properties of and relations between regions. Properties include shape as defined by Fourier analysis of the s–ψ equation of the bounding curve. A typical relation between regions is the degree of adjacency.Finally, the program matches the actual relational structure of the regions of the picture with ideal relational structures representing various objects, using a heuristic search procedure, and selects that object whose relational structure best matches the actual picture."In B.Meltzer and D.Michie (Eds.), Machine intelligence 6. New York: Elsevier, 377-396

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