Hybrid Artificial Intelligence Systems 5th International Confere
th The 5 International Conference on Hybrid Artificial Intelligence Systems (HAIS 2010) has become a unique, established and broad interdisciplinary forum for researchers and practitioners who are involved in developing and applying symbolic and sub-symbo
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Subseries of Lecture Notes in Computer Science
6076
Manuel Graña Romay Emilio Corchado M. Teresa Garcia-Sebastian (Eds.)
Hybrid Artificial Intelligence Systems 5th International Conference, HAIS 2010 San Sebastián, Spain, June 23-25, 2010 Proceedings, Part I
13
Series Editors Randy Goebel, University of Alberta, Edmonton, Canada Jörg Siekmann, University of Saarland, Saarbrücken, Germany Wolfgang Wahlster, DFKI and University of Saarland, Saarbrücken, Germany Volume Editors Manuel Graña Romay Facultad de informatica UPV/EHU San Sebastian, Spain E-mail: [email protected] Emilio Corchado Universidad de Salamanca, Spain E-mail: [email protected] M. Teresa Garcia-Sebastian Facultad de informatica UPV/EHU San Sebastian, Spain E-mail: [email protected]
Library of Congress Control Number: Applied for
CR Subject Classification (1998): I.2, H.3, F.1, H.4, I.4, I.5 LNCS Sublibrary: SL 7 – Artificial Intelligence ISSN ISBN-10 ISBN-13
0302-9743 3-642-13768-7 Springer Berlin Heidelberg New York 978-3-642-13768-6 Springer Berlin Heidelberg New York
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Preface
The 5th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2010) has become a unique, established and broad interdisciplinary forum for researchers and practitioners who are involved in developing and applying symbolic and sub-symbolic techniques aimed at the construction of highly robust and reliable problem-solving techniques, and bringing the most relevant achievements in this field. Overcoming the rigid encasing imposed by the arising orthodoxy in the field of artificial intelligence, which has led to the partition of researchers into so-called areas or fields, interest in hybrid intelligent systems is growing because they give freedom to design innovative solutions to the ever-increasing complexities of real-world problems. Noise and uncertainty call for probabilistic (often Bayesian) methods, while the huge amount of data in some cases asks for fast heuristic (in the sense of suboptimal and ad-hoc) algorithms able to give answers in acceptable time frames. High dimensionality demands linear and non-linear dimensionality reduction and feature extraction algorithms, while the imprecision and vagueness call for fuzzy reasoning and linguistic var
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