A program called "AM" is described which carries on simple mathematics research, defining and studying new concepts under the guidance of a large body of heuristic rules. The 250 heuristics communicate via an agenda mechanism, a global priority queue of small tasks for the program to perform, and reasons why each task is plausible (for example, "Find generalizations of'primes', because'primes' turned out to be so useful a concept"). Each concept is represented as an active, structured knowledge module. One hundred very incomplete modules are initially supplied, each one corresponding to an elementary set-theoretic concept (for example, union). This provides a definite but immense space which AM begins to explore.

J. Do RAN 433 22 Chromosome classification and segmentation as exercises in knowing what to expect.

The eighth volume completes a ten-year span of the Machine Intelligence series. It is appropriate, therefore, to take stock of the main events, and to note certain solid steps and occasional forward leaps. Leaps are normally preceded by some preparatory back-tracking. The uniform procedures of heuristic search and resolution theorem-proving which dominated the scene in 1965 cannot of themselves, as we now see, be developed into "the answer" to automatic problem-solving. This realisation has paved the way for machine-aided forays into non-trivial mathematics, as indicated in Bledsoe and Tyson's contribution to this volume.

Names only appear which are in no sense bibliographic references; otherwise they are to be found in the author index.]

MACHINE INTELLIGENCE 8 Machine Representations of Knowledge Machine Intelligence Volumes 1-7 Editor-in-Chief: Donald Michie are all published by Edinburgh University Press and in the United States of America by Halsted Press (a subsidiary of John Wiley & Sons, Inc.) MACHINE INTELLIGENCE 8 New York - London - Sydney - Toronto The publisher's colophon is reproduced from James Gillison's drawing of the ancient Market Cross, Chichester 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 prior permission.

Virginia Polytechnic Institute and State University Blacksburg, Virginia Philosophers and - "pseudognosticians" (the artificial intelligentsial) are coming more and more to recognize that they share common ground and that each can learn from the other. This has been generally recognized for many years as far as symbolic logic is concerned, but less so in relation to the foundations of probability. In this essay I hope to convince the pseudognostician that the philosophy of probability is relevant to his work. One aspect that I could have discussed would have been probabilistic causality (Good, 1961/62), in view of Hans Berliner's forthcoming paper "Inferring causality in tactical analysis", but my topic here will be mainly dynamic probability. The close relationship between philosophy and pseudognostics is easily understood, for philosophers often try to express as clearly as they can how people make judgments. To parody Wittgenstein, what can be said at all can be said clearly and it can be programmed A paradox might seem to arise. Formal systems, such as those used in mathematics, logic, and computer programming, can lead to deductions outside the system only when there is an input of assumptions. For example, no probability can be numerically inferred from the axioms of probability unless some probabilities are assumed without using the axioms: ex nihilo nihil fit.2 This leads to the main controversies in the foundations of statistics: the controversies of whether intuitive probability3 should be used in statistics and, if so, whether it should be logical probability (credibility) or subjective (personal).

Plan modification *The research reported herein was sponsored by the National Science Foundation under Grant GJ-36146.

The objects that ATRANS operates upon are abstract relationships and the physical instruments of ATRANS are rarely specified. The'trans' that was referred to in the beginning of this paper is what we call ATRANS. ATRANS takes as object the abstract relationship that holds between two real world objects.

To use language one must be able to make inferences about the information which language conveys. This is apparent in many ways. For one thing, many of the processes which we typically consider "linguistic" require inference making. For example, structural disambiguation: (1) Waiter, I would like spaghetti with meat sauce and wine. You would not expect to be served a bowl of spaghetti floating in meat sauce and wine. That is, you would expect the meal represented by structure (2) rather than that represented by (3).

The junction categories and link planting rules of SEE lyzed. That, however, is not the main point; it is merely typical of the way in which the program developed by a process of finding counter-examples that both invalidated old rules and hinted at new ones (Winston, 1973). The need to add and modify rules almost continuously to handle exceptions suggests that there is a basic flaw in the design. The flaw seems to be that Guzman used locally computed picture predicates as evidence for global scene-based properties. To avoid this one must ask what do the lines in the picture depict?