Induction, Algorithmic Learning Theory, and Philosophy
This is the first book to collect essays from philosophers, mathematicians and computer scientists working at the exciting interface of algorithmic learning theory and the epistemology of science and inductive inference. Readable, introductory essays
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LOGIC, EPISTEMOLOGY, AND THE UNITY OF SCIENCE VOLUME 9
Editors Shahid Rahman, University of Lille III, France John Symons, University of Texas at El Paso, U.S.A. Editorial Board Jean Paul van Bendegem, Free University of Brussels, Belgium Johan van Benthem, University of Amsterdam, the Netherlands Jacques Dubucs, University of Paris I-Sorbonne, France Anne Fagot-Largeault, Collège de France, France Bas van Fraassen, Princeton University, U.S.A. Dov Gabbay, King’s College London, U.K. Jaakko Hintikka, Boston University, U.S.A. Karel Lambert, University of California, Irvine, U.S.A. Graham Priest, University of Melbourne, Australia Gabriel Sandu, University of Helsinki, Finland Heinrich Wansing, Technical University Dresden, Germany Timothy Williamson, Oxford University, U.K.
Logic, Epistemology, and the Unity of Science aims to reconsider the question of the unity of science in light of recent developments in logic. At present, no single logical, semantical or methodological framework dominates the philosophy of science. However, the editors of this series believe that formal techniques like, for example, independence friendly logic, dialogical logics, multimodal logics, game theoretic semantics and linear logics, have the potential to cast new light on basic issues in the discussion of the unity of science. This series provides a venue where philosophers and logicians can apply specific technical insights to fundamental philosophical problems. While the series is open to a wide variety of perspectives, including the study and analysis of argumentation and the critical discussion of the relationship between logic and the philosophy of science, the aim is to provide an integrated picture of the scientific enterprise in all its diversity.
Induction, Algorithmic Learning Theory, and Philosophy Edited by
Michèle Friend George Washington University, Washington, D.C., U.S.A.
Norma B. Goethe National University of Cordoba, Cordoba, Argentina
Valentina S. Harizanov George Washington University, Washington, D.C., U.S.A.
A C.I.P. Catalogue record for this book is available from the Library of Congress.
ISBN 978-1-4020-6126-4 (HB) ISBN 978-1-4020-6127-1 (e-book) Published by Springer, P.O. Box 17, 3300 AA Dordrecht, The Netherlands. www.springer.com
Printed on acid-free paper
Cover image: Adaptation of a Persian astrolabe (Brass 1712-13), from the collection of the Museum of the History of Science, Oxford. Reproduced by permission
All Rights Reserved c 2007 Springer No part of this work may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming, recording or otherwise, without written permission from the Publisher, with the exception of any material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work.
To Hilary Putnam
Contents
Editors’ Preface
ix
Acknowledgments
xi
Contributors
xii
1 Introduction to the Philosophy and Mathematics of
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