Predicates
Having established the use of the logic for representing individuals, attention now turns to the problem of constructing predicates that individuals may or may not satisfy. Essentially, all that is required is the definition of a suitable collection of pr
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Springer-Verlag Berlin Heidelberg GmbH
J. W. Lloyd
Logic for Learni ng Learning Comprehensible Theories from Structured Data
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Springer
J.W.Lloyd The Australian National University Research School of Information Sciences and Engineering Computer Sciences Laboratory Canberra ACT 0200 Australia [email protected]
With 14 Figures
Cataloging-in-Publication Data applied for Bibliographic information published by Die Deutsche Bibliothek Die Deutsche Bibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data is available in the Internet at .
ACM Computing Classification (1998): 1.2.6, 1.2.4, 1.2.3 ISSN 1611-2482 ISBN 978-3-642-07553-7 ISBN 978-3-662-08406-9 (eBook) DOI 10.1007/978-3-662-08406-9 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German copyright law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer-Verlag Berlin Heidelberg GmbH. Violations are liable for prosecution under the German Copyright Law.
http://www.springer.de © J. W. Lloyd 2003 Originally published by Springer-Verlag Berlin Heidelberg New York in 2003 Softcover reprint of the hardcover I st edition 2003 The use of general descriptive names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
Cover design: KiinkelLopka, Heidelberg Typesetting: Camera-ready by the author Printed on acid-free paper 45/3142SR- 54 3 2 1 0
To Susan, Simon, and Patrick
Preface
This book is concerned with the rich and fruitful interplay between the fields of computational logic and machine learning. The intended audience is senior undergraduates, graduate students, and researchers in either of those fields. For those in computational logic, no previous knowledge of machine learning is assumed and, for those in machine learning, no previous knowledge of computational logic is assumed. The logic used throughout the book is a higher-order one. Higher-order logic is already heavily used in some parts of computer science, for example, theoretical computer science, functional programming, and hardware verification, mainly because of its great expressive power. Similar motivations apply here as well: higher-order functions can have other functions as arguments and this capability can be exploited to provide abstractions for knowledge representation, methods for constructing predicates, and a foundation for logic-based computation. The book should be of interest to researchers in machine learning, especially those who study learning methods for structured data. Machine learning
