Representing Examples by Examples
The problem addressed in this paper is: given a set of examples representing some target concept, construct a minimal subset consisting of the most representative examples from which the original set could be easily and accurately reconstructed based on d
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Series Editors: The Rectors of CISM Sandor Kaliszky - Budapest Mahir Sayir - Zurich Wilhe1m Schneider - Wien The Secretary General of CISM Giovanni Bianchi - Milan Executive Editor Carlo Tasso - Udine
The series presents lecture notes, monographs, edited works and proceedings in the field of Mechanics, Engineering, Computer Science and Applied Mathematics. Purpose of the series in to make known in the international scientific and technical community results obtained in some of the activities organized by CISM, the International Centre for Mechanical Sciences.
INTERNATIONAL CENTRE FOR MECHANICAL SCIENCES COURSES AND LECTURES - No. 363
Proceedings of the ISSEK94 Workshop on
MATHEMATICAL AND STATISTICAL METHODS IN ARTIFICIAL INTELLIGENCE EDlTEDBY G. DELLA RICCIA
UNIVERSITY OF UDINE
AND R. KRUSE UNIVERSITY OF BRAUNSCHWEIG
AND R. VIERTL TECHNICAL UNIVERSITY OF WIEN
SPRINGER-VERLAG WIEN GMBH
Le spese di stampa di questo volume sono in parte coperte da contributi deI Consiglio Nazionale delle Ricerche.
This volume contains 32 illustrations
This work is subject to copyright. All rights are reserved,
whether the whole or part of the material is concemed specifically those of translation, reprinting, re-use of illustrations, broadcasting, reproduction by photocopying machine or similar means, and storage in data banks.
© 1995 by Springer-Verlag Wien Originally published by Springer-Verlag Wien New York in 1995
In order to make this volume available as economically and as rapidly as possible the authors' typescripts have been reproduced in their original forms. This method unfortunately has its typographical limitations but it is hoped that they in no way distract the reader.
ISBN 978-3-211-82713-0 DOI 10.1007/978-3-7091-2690-5
ISBN 978-3-7091-2690-5 (eBook)
PREFACE
This volume contains the papers accepted for presentation at the invitationaiISSEK94 Workshop on "Mathematical and Statistical Methods in Artificial Intelligence" organized by the International School for the Synthesis of Expert Knowledge (ISSEK) and held at the Centre International des Sciences Mecaniques (CISM) in Udinefrom September 6 to 8,1994.
In recent years it has become apparent that an important part of the theory of Artificial Intelligence is concerned with reasoning on the basis of uncertain, incomplete or inconsistent information. Classical logic and probability theory are only partially adequate for this, and a variety of other formalisms have been developed, some of the most important being fuzzy methods, possibility theory, belief function theory, non monotonie logics and modallogics.
The aim of this workshop was to contribute to the elucidation of similarities and differences between the formalisms mentioned above. The talks were given by researchers that are welf known in their respective field and the discussion was focused on such topics as fuzzy data analysis, probabilistic reasoning, learning and abduction and logics in uncertainty. Moreover there
was an interesting session on industrial applications with talks given b
