From Synapses to Rules: Discovering Symbolic Rules from Neural Processed Data PDF
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From Synapses to Rules Discovering Symbolic Rules from Neural Processed Data From Synapses to Rules Discovering Symbolic Rules from Neural Processed Data Edited by Bruno Apolloni Department of Information Science University of Milan Milan, Italy and Franz Kurfess Department of Computer Science California Polytechnic State University San Luis Obispo, California Springer-Science+Business Media, LLC Proceedings of the International School on Neural Nets "E.R. Caianiello" Fifth Course: From Synapses to Rules: Discovering Symbolic Rules From Neural Processed Data, held February 25-March 7, 2002, in Erice, Sicily, Italy ISBN 978-1-4613-5204-4 ISBN 978-1-4615-0705-5 (eBook) DOI 10.1007/978-1-4615-0705-5 ©2002 Springer Science+Business Media New York Originally published by Kluwer Academic/Plenum Publishers, New York in 2002 Softcover reprint of the hardcover 1st edition 2002 http ://www. wkap.nl/ 10 9 8 7 6 5 4 3 21 A CLP. record for this book is available from the Library of Congress All rights reserved No part of this book 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. Preface One high-level ability of the human brain is to understand what it has learned. This seems to be the crucial advantage in comparison to the brain activity of other primates. At present we are technologically almost ready to artificially reproduce human brain tissue, but we still do not fully understand the information processing and the related biological mechanisms underlying this ability. Thus an electronic clone of the human brain is still far from being realizable. At the same time, around twenty years after the revival of the connectionist paradigm, we are not yet satisfied with the typical subsymbolic attitude of devices like neural networks: we can make them learn to solve even difficult problems, but without a clear explanation of why a solution works. Indeed, to widely use these devices in a reliable and non elementary way we need formal and understandable expressions of the learnt functions. These must be susceptible of being tested, manipulated and composed with other similar expressions to build more structured functions as a solution of complex problems via the usual deductive methods of the Artificial Intelligence. Many effort have been steered in this directions in the last years, constructing artificial hybrid systems where a cooperation between the sub symbolic processing of the neural networks merges in various modes with symbolic algorithms. In parallel, neurobiology research keeps on supplying more and more detailed explanations of the low-level phenomena responsible for mental processes. This book aims at collecting recent contributions in these fields with the ambitious goal of providing some guidelines for examining the whole process, from the acquisition of sensory data to their symbolic explanation. We identify the early processing of these data with the typical connectionist v VI Preface computations implemented by either biological or artificial neural networks and expect as final output of the process a set of formal rules. That is what we evocatively call a trip from synapses to rules. We organized the contributions in three tracks. A theoretical track examines the mathematical foundations of learning, allowing us a unitary approach for both subsymbolic and symbolic attitudes. The task of the former can be a little relaxed, as it is responsible for extracting elementary properties from data, whose mutual relations will be discovered and further manipulated at the symbolic level. This manipulation is framed in a backtracking procedure that is the symbolic companion of the backpropagation methods. In one direction formulas are composed to describe higher level properties of the data; in the back one formulas are remeditated to be simplified enough to be understandable to the users. This phase is variously managed in the frame of fuzzy set theory under different entropic criteria, and the unifying approach of utilizing an approximation of a formula in order to increase its understandability. A nature driven track relates learning algorithms to the physics of both the training set generation and of the brain processing. Though mapping brain activities to mind functionalities is a hard task far from getting an exhaustive conclusion, in the book we state some links between the two sets. Mostly, we examine some evidence concerning the physics of the brain activity in relation to some competencies of the mind. We also investigate information processing architectures and procedures that are based on this evidence, and may explain some cognitive processes at various complexity levels. An interesting aspect of these models is that they show a good fit with the mathematical models discussed in the previous track. We close the loop of information generation by also considering the processing of the signals generated by a human brain and communicated in a verbal or non verbal communication mode to the sensors of another human brain who tries learning something from them. The third track is systemic. Facing logical and physiological hints coming from the previous tracks, here we want to implement the trip from synapses to rules. This track has a twofold profile. On one hand we clarify the terms of the problem, discussing benefits and draw-backs of the sub symbolic and symbolic approaches for knowledge representation, and trying to clarify misconceptions and myths about connectionism and neural netwrok. On the other hand, systems are presented that realize an integration between procedures dealing with immediate signals and those managing symbols. We provide both an overview of these systems and proposals for new approaches, where integration stands for either a tight cooperation between the two different kinds of procedures, or an inherent mixture of Preface vii conceptual tools representative of the two approaches into a single procedure. This book is actually a collection of contributions that we tried to integrate into a unified body. We do not assume that it provides a complete theory for supporting complete applications. Rather, we aim at outlining a solid framework for further developments in the field. With this aim we group papers of the same track into specific part of the book, each starting with a preface trying to supply a common frame of reference for the various articles. Analogously we end the part with a section that resumes concluding remarks from the respective papers. These sections were written by the Editors, with a special contribution of Gabriele Biella in the premise of the second track. Whether these developments are grounded in the field of cognitive science or oriented towards computational applications, we hope that this book helps narrowing the divide between the two perspectives. Our goal here actually is twofold: understanding how information processing works in a human brain is a powerful way for improving our computational capabilities, and more sophisticated computational models lead to a better comprehension of cognitive aspects of the human mind. Despite its deficiencies and crutches, an electronic clone of some functions of our brain seems to be the most powerful computing device for solving some highly complex computational problems. Bruno Apolloni and Franz Kurfess Acknowledgments This volume is the result of interactions and cooperation among many people. We thank the authors of the chapters first for their enthusiastic and constructive lectures at the Course "From synapses to rules: discovering symbolic rules from neural processed data", organized in Erice (Italy) by the International School on Neural Nets "E. R. Caianiello". These lectures make up in fact the basis of the book. We also thank the school's students for their intellectual contribution, some of which fonn chapters in the book. A special mention goes to the young assistants of laren lab (http://laren.dsi.unimi.it): beside co-authoring some of the chapters, they made a major technical contribution in editing the volume. ix Contributors • Luigi F. Agnati, Department of BioMedical Sciences, University of Modena, Italy, [email protected] • Bruno Apolloni, Dipartimento di Scienze dell'Informazione, Universita degli Studi di Milano, Via Comelico 39/41 20135 Milano Italy, [email protected] • Stefano Baraghini, Dipartimento di Scienze dell'Informazione, Universita degli Studi di Milano Via Comelico 39/41 20135 Milano Italy, [email protected] • Simone Bassis, Dipartimento di Scienze dell'Informazione, Universita degli Studi di Milano, Via Come1ico 39/41 20135 Milano Italy, [email protected] • F. Benfenati, Department of Experimental Medicine, Section of Human Physiology, University of Genova, Italy • Gabriele E. M. Biella, Istituto di Neuroscienze e Bioimmagini, Consiglio Nazionale delle Ricerche, Via Fratelli Cervi 93 20090 Segrate (MI) Italy, [email protected] • Anna Esposito, Department of Computer Science and Engineering, Wright State University, 3640 Col. Glenn Hwy., Dayton, Ohio, USA, [email protected] • Massimo Ferri, Department of Mathematics, University of Bologna, Italy, [email protected] • Kjell Fuxe, Department of Neuroscience, Karolinska Institutet, Sweden, [email protected] • Sabrina Gaito, Dipartimento di Matematica "F. Enriques", Universita degli Studi di Milano, Via Saldini 50 20133 Milano Italy, [email protected] Xl xu Contributors • Stefania Gentili, Department of Mathematics and Computer Science, DIMI, University of Udine, Via delle Scienze 208, Udine Italy, [email protected] • Marco Gori, Dipartimento di Ingegneria dell'Infonnazione, Via Roma, 5653100 Siena Italy • Domenico Iannizzi, Dipartimento di Scienze dell'Infonnazione, Universita degli Studi di Milano, Via Comelico 39/41 20135 Milano Italy, [email protected] • Franz J. Kurfess, Computer Science Department, California Polytechnic State University, San Luis Obispo CA USA, [email protected] • Dario Malchiodi, Dipartimento di Scienze dell'Infonnazione, Universita degli Studi di Milano, Via Comelico 39/41 20135 Milano Italy, [email protected] • Maria Marinaro, Dipartimento di Fisica "E. R. Caianiello" Universita di Salerno Baronissi (SA); INFM Sezione di Salerno; and IIASS Vietri suI Mare (SA), Italy, [email protected] • Corrado Mencar, Dipartimento di Infonnatica Universita degli Studi di Bari Via Orabona 4 70125 Bari Italy, [email protected] • Anna Morpurgo, Dipartimento di Scienze dell'Infonnazione, Universita degli Studi di Milano, Via Comelico 39/41 20135 Milano Italy, [email protected] • Daniele Mundici, Dipartimento di Scienze dell'Infonnazione, Universita degli Studi di Milano, Via Comelico 39/41 20135 Milano Italy, [email protected] • Christos Orovas, Dipartimento di Scienze dell'Infonnazione, Universita degli Studi di Milano, Via Comelico 39/41 20135 Milano Italy, [email protected] • Giorgio Palmas, ST Microelectronics, Agrate Brianza (MI) Italy, [email protected] • Asim Roy, School of Infonnation Systems, Arizona State University, Tempe, AZ, USA, [email protected] • L. M. Santarossa, Department of BioMedical Sciences, University of Modena, Italy • Silvia Scarpetta, Dipartimento di Fisica "E. R. Caianiello" Universita di Salerno Baronissi (SA); INFM Sezione di Salerno; and IIASS Vietri suI Mare (SA) Italy, [email protected] • Ron Sun, CECS Department, University of Missouri-Columbia, [email protected] Contributors Xlll • Anna Maria Zanaboni, Dipartimento di Scienze dell'Infonnazione, Universita degli Studi di Milano Via Comelico 39/41 20135 Milano Italy, [email protected]
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