Such methods can effectively and efficiently induce good descriptions from a given set of examples and, optionally, induce counter-examples (for example Michalski, 1975, 1980a; Quinlan, 1979; Langley et al., 1983). These methods cannot modify concept descriptions which are contradicted by new examples, but must re-learn the descriptions from scratch. In contrast, incremental learning methods modify concept descriptions to accommodate new learning events (Winston, 1975; Michalski and Larson, 1978). When we observe human learning we clearly see that it is incremental.

It is a rich source of difficult and challenging problems which involve issues of knowledge representation, the analysis of natural language, and the automation of practical and common-sense reasoning.

This paper describes some work on automatically generating finite counterexamples in topology, and the use of counterexamples to speed up proof discovery in intermediate analysis, and gives some examples theorems where human provers are aided in proof discovery by the use of examples.

The notion of an ethical machine can be interpreted in more than one way. Perhaps the most important interpretation is a machine that can generalize from existing literature to infer one or more consistent ethical systems and can work out their consequences. An ultra-intelligent machine should be able to do this, and that is one reason for not fearing it. INTRODUCTION There is fear that'the machine will become the master', especially compounded by the possibility that the machine will go wrong. There is, for example, a play by E. M. Foster based on this theme.

We are interested in constructing a computer agent whose behaviour will be intelligent enough to perform cooperative tasks involving other agents like itself. The construction of such agents has been a major goal of artificial intelligence research. One of the key tasks such an agent must perform is to form plans to carry out its intentions in a complex world in which other planning agents also exist. To construct such agents, it will be necessary to address a number of issues that concern the interaction of knowledge, actions, and planning. Briefly stated, an agent at planning time must take into account what his future states of knowledge will be if he is to form plans that he can execute; and if he must incorporate the plans of other agents into his own, then he must also be able to reason about the knowledge and plans of other agents in an appropriate way.

A programming language based on sets; motivation and examples.

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.

Networks as models of word storage: G. R. Kiss

People's certainty of the past is D follows from A. B. and C. It may be that A. B. and C, limited by the fidelity of the devices that record it, their though certain, suggest but do not confirm D. in which case knowledge of the present is always incomplete, and their the number associated with D might be less than the 1.0 that knowledge of the future is but speculation. Even though usually represents certainty in such systems. If A. B. or C nothing is certain, people behave as if almost nothing is are uncertain, then the number associated with D is modified uncertain. They are adept at discounting uncertainty -- to account for the uncertainty of its premises. These numbers making it go away. This article discusses how Al programs are given different names by different authors; we refer might be made similarly adept.