A survey of early work exploring how AI can be used in medicine, with somewhat more technical expositions than in the complementary volume Artificial Intelligence in Medicine."Each chapter is preceded by a brief introduction that outlines our view of its contribution to the field, the reason it was selected for inclusion in this volume, an overview of its content, and a discussion of how the work evolved after the article appeared and how it relates to other chapters in the book.

JANICE S. AIKINS Dr. Aikins received her Ph.D. in computer science from Stanford University in 1980. She is currently a research computer scientist at IBM's Palo Alto Scientific Center. She specializes in designing systems with an emphasis on the explicit representation of control knowledge in expert systems. ROBERT L. BLUM Dr. Blum received his M.D. from the University of California Medical School at San Francisco in 1973. From 1973 to 1976 he did an internship and residency in the Department of Internal Medicine at the Kaiser Foundation Hospital in Oakland, California, where he was chief resident in 1976.

This paper documents an investigation into the role that the late Alan Turing played in the development of electronic computers. Evidence is presented that during the war he was associated with a group that designed and built a series of special purpose electronic computers, which were in at least a limited sense'program controlled', and that the origins of several post-war general purpose computer projects in Britain can be traced back to these wartime computers. INTRODUCTION During my amateur investigations into computer history, I grew intrigued by the lack of information concerning the role played by the late Alan Turing.

In this paper we will be concerned with such reasoning in its most general form, that is, in inferences that are defeasible: given more information, we may retract them. The purpose of this paper is to introduce a form of non-monotonic inference based on the notion of a partial model of the world. We take partial models to reflect our partial knowledge of the true state of affairs. We then define non-monotonic inference as the process of filling in unknown parts of the model with conjectures: statements that could turn out to be false, given more complete knowledge. To take a standard example from default reasoning: since most birds can fly, if Tweety is a bird it is reasonable to assume that she can fly, at least in the absence of any information to the contrary. We thus have some justification for filling in our partial picture of the world with this conjecture. If our knowledge includes the fact that Tweety is an ostrich, then no such justification exists, and the conjecture must be retracted.

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.

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

INTRODUCTION This paper is a report on some preliminary work undertaken as part of a longer term study of the problems which arise in designing and implementing digital computer programs which'learn'. A program has been written which learns to play the board game'Go-Moku' using a particular learning mechanism to be described later. The program is to be regarded as an experimental tool by means of which the particular learning mechanism can be investigated in some depth. Go-Moku is a simple but not a trivial game with an intellectual content comparable with a game of draughts (checkers). Opinions have sometimes been expressed that there is nothing to be learnt (no pun intended!) by programming simple games. Present knowledge of programming learning is such that it is useful to experiment with programs operating in a simple task environment. It is not so much what game the program learns as how it learns it. It is emphasised that the object of the present work is not to write a program which plays a difficult game better than anyone or anything has played it before, but to isolate and investigate particular aspects of a learning process which might be valid over a range of ill-structured problems. For the record, however, the current learning programs learn to play a good (basically defensive) game. The modifications currently being made to the program should give it a learning capacity to become unbeatable.

In recent years there has been a great deal of interest in the commercial applications of knowledge-based (KB) systems (commonly called expert systems). Interest in KB systems was spurred on by the development of programs that can solve complex tasks at an expert level.

Stanford -- KSL that Think. Consideration of the of the new 16-bit integrated circuits that phenomenal progress of the past 30 years leaves one with a feeling of have allowed computers oi small size and considerable power to be developed. The only certainty in sight is that scientists. BRUCE G. BUCHANAN is Professor of In addition to game playing early Al work focused on techniques for solving Computer Science Research at Stanford small symbolic reasoning problems. Researchers continue to ponder these problems (Overleat) Illustration by f red Nelson as well.

Current subprojects include applications of the program Gerring.