Interval / Probabilistic Uncertainty and Non-classical Logics
Editors: Huynh, V.-N., Nakamori, Y., Ono, H., Lawry, J., Kreinovich, V., Nguyen, H.T. (Eds.)
Free Preview- Proceedings of the International Workshop on Interval/Probabilistic Uncertainty and Non Classical Logics (UncLog'08), Ishikawa, Japan, March 25-28, 2008
- Recent developments in Uncertainty and Non-classical Logics
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- About this book
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Most successful applications of modern science and engineering, from discovering the human genome to predicting weather to controlling space missions, involve processing large amounts of data and large knowledge bases. The ability of computers to perform fast data and knowledge processing is based on the hardware support for super-fast elementary computer operations, such as performing arithmetic operations with (exactly known) numbers and performing logical operations with binary ("true"-"false") logical values. In practice, measurements are never 100% accurate. It is therefore necessary to find out how this input inaccuracy (uncertainty) affects the results of data processing. Sometimes, we know the corresponding probability distribution; sometimes, we only know the upper bounds on the measurement error -- which leads to interval bounds on the (unknown) actual value. Also, experts are usually not 100% certain about the statements included in the knowledge bases. A natural way to describe this uncertainty is to use non-classical logics (probabilistic, fuzzy, etc.).
This book contains proceedings of the first international workshop that brought together researchers working on interval and probabilistic uncertainty and on non-classical logics. We hope that this workshop will lead to a boost in the much-needed collaboration between the uncertainty analysis and non-classical logic communities, and thus, to better processing of uncertainty.
- Table of contents (28 chapters)
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An Algebraic Approach to Substructural Logics – An Overview
Pages 3-4
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On Modeling of Uncertainty Measures and Observed Processes
Pages 5-15
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Fast Algorithms for Computing Statistics under Interval Uncertainty: An Overview
Pages 19-31
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Trade-Off between Sample Size and Accuracy: Case of Static Measurements under Interval Uncertainty
Pages 32-44
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Trade-Off between Sample Size and Accuracy: Case of Dynamic Measurements under Interval Uncertainty
Pages 45-56
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Table of contents (28 chapters)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Interval / Probabilistic Uncertainty and Non-classical Logics
- Editors
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- Van-Nam Huynh
- Yoshiteru Nakamori
- Hiroakira Ono
- Jonathan Lawry
- Vladik Kreinovich
- Hung T. Nguyen
- Series Title
- Advances in Intelligent and Soft Computing
- Series Volume
- 46
- Copyright
- 2008
- Publisher
- Springer-Verlag Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
- eBook ISBN
- 978-3-540-77664-2
- DOI
- 10.1007/978-3-540-77664-2
- Softcover ISBN
- 978-3-540-77663-5
- Series ISSN
- 1867-5662
- Edition Number
- 1
- Number of Pages
- XVIII, 376
- Number of Illustrations
- 79 b/w illustrations
- Topics
