INTRODUCTION This paper describes some recent experiments with a computer program which is capable of useful, or at least interesting, application to a number of different problems. The program, the Graph Traverser, has been described in detail in a previous paper (Doran & Michie 1966). However, we shall here need to view the basic algorithm from a rather more general standpoint, corresponding to an actual extension in the flexibility of the program, so that a restatement of what the program can do is desirable. The Graph Traverser, which is written in Elliott 4100 Algol, is potentially applicable to problem situations which can be idealised in the following way (see for comparison Newell and Ernst 1965). There is given a set of'states', which are connected by a set of'transformations', or, as I shall call them, 'operators'. An operator will be applicable to some, but not necessarily all, of the states and two distinct operators applied to either the same or distinct states may each give the same state as end-product. Most of the concepts to be used here which are related to the use of operators were discussed in a paper by Michie (1967). This type of problem situation is represented in Figure 1 by a graph (in the mathematical sense) to which have been added various labels. In this representation states correspond to nodes of the graph, and operators to labelled arcs--a, b, c in this quite arbitrary case. Notice that associated with each node (or state) is a triad of integers.

JUSTIFICATION OF CHESS PROGRAMS Young animals play games in order to prepare themselves for the business of serious living, without getting hurt in the training period. Game-playing on computers serves a similar function. It can teach us something about the structure of thought processes and the theory of struggle and has the advantage over economic modelling that the rules and objectives are clear-cut. If the machine wins tournaments it must be a good player. The complexity and originality of a master chess player is perhaps greater than that of a professional economist. The chess player continually pits his wits against other players and the precision of the rules makes feasible a depth of thinking comparable to that in mathematics. No program has yet been written that plays chess of even good amateur standard. A really good chess program would be a breakthrough in work on machine intelligence, and would be a great encouragement to workers in other parts of this field and to those ...

The formulation of the resolution principle by J. A. Robinson (1965a) has provided the impetus for a number of recent efforts in automatic theoremproving. In particular, the program PG1 (Wos et al. 1964, 1965), written by L. Wos, G. A. Robinson and D. F. Carson for the Control Data 3600, utilises the resolution principle in conjunction with a'unit preference strategy' and a'set of support strategy' to produce efficient proofs in first-order functional logic and group theory. These programs have generated proofs of some interesting propositions of number theory, in addition to theorems of first-order functional logic and group theory. A'literal' is an n-place predicate expression F(xi, x2,.-.., x) or its negation F(xi, x2,., x „) whose arguments are individual variables, individual constants, or functional expressions. Quantifiers do not occur in these formulae, since existentially quantified variables have been replaced by functions of universally quantified ones, and the remaining variables may therefore be taken as universally quantified.

INTRODUCTION A series of computer programs have been written to play the board game Go-Moku. Go-Moku is played on a 19 x 19 square mesh. Player b(w) has a supply of black (white) pieces. The players take it in turns to play a piece on a mesh point. The winner is the first player to complete a 5-pattern, that is, to make up a (horizontal, vertical or diagonal) line of five and only five adjacent pieces of his colour. The programs carry out a backtrack analysis on games which have been lost to a human opponent.

In attaining these objectives certain other desirable features of programming languages had to be relegated to secondary importance: (vi) Fast arithmetical facilities on integer and real numbers.

Lisp, etc.) are designed for off-line use. Poi,-1 is for use by a person communicating directly with a computer via a typewriter. With this in mind I have aimed at a tolerable efficiency of execution, and an ability to define and name new operations, with comprehensive monitoring facilities. On the other hand actual error messages are rather simple. At the very lowest level, the computer can be used just as a desk calculator.

The two outstanding figures in the history of computer science are Alan Turing and John von Neumann, and they shared the view that logic was the key to understanding and automating computation. In particular, it was Turing who gave us in the mid-1930s the fundamental analysis, and the logical definition, of the concept of'computability by machine' and who discovered the surprising and beautiful basic fact that there exist universal machines which by suitable programming can be made to t This essay is an expanded and revised version of one entitled The Role of Logic in Computer Science and Artificial Intelligence, which was completed in January 1992 (and was later published in the Proceedings of the Fifth Generation computer Systems 1992 Conference). Since completing that essay I have had the benefit of extremely helpful discussions on many of the details with Professor Donald Michie and Professor I. J. Good, both of whom knew Turing well during the war years at Bletchley Park. Professor J. A. N. Lee, whose knowledge of the literature and archives of the history of computing is encyclopedic, also provided additional information, some of which is still unpublished. Further light has very recently been shed on the von Neumann side of the story by Norman Macrae's excellent biography John von Neumann (Macrae 1992). Accordingly, it seemed appropriate to undertake a more complete and thorough version of the FGCS'92 essay, focussing somewhat more on the interesting historical and biographical issues. I am grateful to Donald Michie and Stephen Muggleton for inviting me to contribute such a'second edition' to the present volume, and I would also like to thank the Institute for New Computer Technology (ICOT) for kind permission to make use of the FGCS'92 essay in this way. 1 LOGIC, COMPUTERS, TURING, AND VON NEUMANN

EBL systems 117 see also non-deterministic finite see also explanation-based learning automata device malfunction data 165 ECG interpretation 306-7 description length of propositions Eckert, J.P. 4, 11 obtained 164 Eckert-Mauchly computers 11 Diagram Configuration (DC) model, EDSAC computer 11 perceptual chunks used EDVAC computer 11 420

OXFORD 1994 Oxford University Press, Walton Street, Oxford 0X2 6DP Oxford New York Athens Auckland Bangkok Bombay Calcutta Cape Town Dar es Salaam Delhi Florence Hong Kong Istanbul Karachi Kuala Lumpur Madras Madrid Melbourne Mexico City Nairobi Paris Singapore Taipei Tokyo Toronto and associated companies in Berlin lbadan Published in the United States by Oxford University Press Inc., New York 0 E. K. Furukawa, D. Michie, and S. Muggleton, 1994 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, without the prior permission in writing of Oxford University Press. Enquiries concerning reproduction outside those terms and in other countries should be sent to the Rights Department, Oxford University Press, at the address above. This book is sold subject to the condition that it shall not, by way of trade or otherwise, be lent, re-sold, hired out, or otherwise circulated without the publisher's prior consent in any form of binding or cover other than that in which it is published and without a similar condition including this condition being imposed on the subsequent purchaser. The founder of modern computational logic, J.A. Robinson, opens this volume with a chapter on the field's great forefathers John von Neumann and Alan Turing.

Turing's best known work is concerned with whether universal machines can decide the truth value of arbitrary logic formulae. However, in this paper it is shown that there is a direct evolution in Turing's ideas from his earlier investigations of computability to his later interests in machine intelligence and machine learning. Turing realised that machines which could learn would be able to avoid some of the consequences of Godes and his results on incompleteness and undecidability. Machines which learned could continuously add new axioms to their repertoire. Inspired by a radio talk given by Turing in 1951, Christopher Strachey went on to implement the world's first machine learning program.