Abstraction, Reformulation, and Approximation 7th International
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Subseries of Lecture Notes in Computer Science
4612
Ian Miguel Wheeler Ruml (Eds.)
Abstraction, Reformulation, and Approximation 7th International Symposium, SARA 2007 Whistler, Canada, July 18-21, 2007 Proceedings
13
Series Editors Jaime G. Carbonell, Carnegie Mellon University, Pittsburgh, PA, USA Jörg Siekmann, University of Saarland, Saarbrücken, Germany Volume Editors Ian Miguel University of St.Andrews School of Computer Science North Haugh, KY16 9SX, St.Andrews, UK E-mail: [email protected] Wheeler Ruml Palo Alto Research Center 3333 Coyote Hill Road, Palo Alto, CA 94304, USA E-mail: [email protected]
Library of Congress Control Number: 2007930461
CR Subject Classification (1998): I.2, F.4.1, F.3 LNCS Sublibrary: SL 7 – Artificial Intelligence ISSN ISBN-10 ISBN-13
0302-9743 3-540-73579-8 Springer Berlin Heidelberg New York 978-3-540-73579-3 Springer Berlin Heidelberg New York
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Preface
It has been recognized since the inception of artificial intelligence that abstractions, problem reformulations and approximations (AR&A) are central to human common-sense reasoning and problem solving and to the ability of systems to reason effectively in complex domains. AR&A techniques have been used in a variety of problem-solving settings, including automated reasoning, cognitive modelling, constraint programming, design, diagnosis, machine learning, modelbased reasoning, planning, reasoning, scheduling, search, theorem proving, and intelligent tutoring. The primary use of AR&A techniques in such settings has been to overcome computational intractability by decreasing the combinatorial costs associated with searching large spaces. In addition, AR&A techniques are useful for knowledge acquisition and explanation generation in complex domains. The considerable interest in AR&A techniques has led to a series of successful symposia over the last decade, the Symposium on Abstraction, Reformulation, and Approximation (SARA). Its aim is to provide a forum for intensive interaction among researchers in all areas of artificial intelligence and computer science interested in the different aspects of AR&A. AAAI workshops in 1990 and 1992 focused on selecting, constructi
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