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.

Machine Intelligence 1 (1967) (eds N. Collins and D. Michie) Oliver & Boyd, Edinburgh Machine Intelligence 2 (1968) (eds E. Dale and D. Michie) Oliver & Boyd, Edinburgh (1 and 2 published as one volume in 1971 by Edinburgh University Press) (eds N. Collins, E. Dale, and D. Michie). CLARENDON PRESS OXFORD 1988 Oxford University Press, Walton Street, Oxford 0X2 6DP Oxford New York Toronto Delhi Bombay Calcutta Madras Karachi Petaling Jaya Singapore Hong Kong Tokyo Nairobi Dar es Salaam Cape Town Melbourne Auckland and associated companies in Berlin lbadan Oxford is a trade mark of Oxford University Press Published in the United States by Oxford University Press, New York J. E. Hayes, D. Michie, and J. Richards 1988 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, electronic, mechanical, photocopying, recording, or otherwise, without the prior permission of Oxford University Press British Library Cataloguing in Publication Data Machine Intelligence. Richard J. 006.3 ISBN 0-19-853718-2 Library of Congress Cataloging in Publication Data Data available Typeset and printed in Northern Ireland at The Universities Press (Belfast) Ltd. Held at intervals in Scotland, the first seven International Machine Intelligence Workshops spanning the period of 1965-71 were involved in developing the new subject internationally--in those early days mainly as a mid-Atlantic phenomenon.

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.

Seven years or so after it was first proposed (Kowalski 1974), the technique of'logic programming' today has an enthusiastic band of users and an increasingly impressive record of applications. For most of these people, logic progamming means PROLOG, the system defined and originally implemented by the Marseille group (Roussel 1975). PROLOG has since been implemented in several other places, most notably at Edinburgh (Warren et al. 1977). Much of the rapid success of logic progamming is due to these implementations of PROLOG (as well as to the inspired missionary work of Kowalski, van Emden, Clark and others). The Edinburgh PROLOG system is in particular a superb piece of software engineering which allows the logic progammer to compile assertions into DEC-10 machine code and thus run logic programs with an efficiency which compares favourably with that of compiled LISP. All other implementations of logic programming (including our own, which we describe in this paper) are based on interpreters.

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.

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

"Learning", the incorporation of additional knowledge into expert systems, ranges from human data entry (learning by being told), to data gathering (learning by observing), to full-fledged theory formation (learning by discovery). One important kind of learning is the compiling of descriptive meta-knowledge into strategic form, recasting it into a form in which it can be evaluated efficiently. Much of what we earlier called strategic meta-knowledge may be seen to be operationalized "caches" of descriptive or systemic meta-knowledge. For instance, R9 and R10 can be converted from systemic to strategic form by slight rewordings of their actions.

We have developed and partially Imp;zmented an "automated consultant" called SACON (Structural Analysis CONsultant), using the EMYCIN system as Its framework. SACON advises non expert engineers in the use of a large, general-purpose structural analysis program. The structure of the knowledge b,:se, including the major concepts used and Inferences drawn by the consultant, is presented. We conclude by making some observations 11 light of this application about the EMYCIN system as a representational vehicle and the process of acquiring knowledge for rule-based systems. Key words: knowledge-based systems, knowledge acquisition, knowledge representation, automated consultant, structural analysis, inference structure. This research was supported by the Defense Advanced Research Projects Agency (ARPA Order No. 2494 Contract No. DAHC15-73-C-0435) and the Air Force Flight Dynamics Laboratory. Reprinted from the Sixth International Joint Conference on Artificial Intelligence, Tokyo, Japan, August 1979. Used by permission of the International Joint Conference on Artificial Intelligence, Inc.; copies of the Proceedings are available from Morgan Kaufmann Publishers, Inc., 95 First Street, Los Altos, CA 94022, USA.

It has been noted that the clinical computer programs The more "human-like" the consultation system, the most acceptable to physicians are those which more likely that the busy physician will see it as a perform tasks that the physician himself is either viable alternative to interaction with a human consultant.

Case-based reasoning is used extensively by people in A second driving force in the evolutionary history of CBR both expert and commonsense situations. It provides a was dissatisfaction with rule-based reasoning (expert systems wide range of advantages.