Knowledge Representation Techniques: A Rough Set Approach

Overview

This book contains a cohesive, self-contained collection of theoretical and applied research results that have been achieved in this project which pertain to nonmonotonic and approximate easoning systems developed for an experimental unmanned aerial vehicle system used in the project. This book should be of interest to the theoretician and applied researcher alike and to autonomous system developers and software agent and intelligent system developers.

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Paperback (Softcover reprint of hardcover 1st ed. 2006)
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Overview

This book contains a cohesive, self-contained collection of theoretical and applied research results that have been achieved in this project which pertain to nonmonotonic and approximate easoning systems developed for an experimental unmanned aerial vehicle system used in the project. This book should be of interest to the theoretician and applied researcher alike and to autonomous system developers and software agent and intelligent system developers.

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Editorial Reviews

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"Knowledge representation is one of the most important elements of Artificial Intelligence, representing the study of how knowledge about the world can be represented and what kinds of reasoning can be done with that knowledge. The book contains three parts and is founded on the concept of rough sets. … This book is recommended to researchers interested in studying and applying rough set theory in various domains." (Ion Iancu, Zentralblatt MATH, Vol. 1131 (9), 2008)

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Product Details

  • ISBN-13: 9783642070129
  • Publisher: Springer Berlin Heidelberg
  • Publication date: 11/23/2010
  • Series: Studies in Fuzziness and Soft Computing Series , #202
  • Edition description: Softcover reprint of hardcover 1st ed. 2006
  • Edition number: 1
  • Pages: 342
  • Product dimensions: 0.73 (w) x 6.14 (h) x 9.21 (d)

Table of Contents

and Preliminaries.- Basic Notions.- Rough Sets.- Relational and Deductive Databases.- Non-Monotonic Reasoning.- From Relations to Knowledge Representation.- Rough Knowledge Databases.- Combining Rough and Crisp Knowledge.- Weakest Sufficient and Strongest Necessary Conditions.- CAKE: Computer Aided Knowledge Engineering.- Formalization of Default Logic Using CAKE.- A UAV Scenario: A Case Study.- From Sensors to Relations.- Information Granules.- Tolerance Spaces.- A Rough Set Approach to Machine Learning.- UAV Learning Process: A Case Study.

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