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Transactions on Rough Sets IX pp 135–144Cite as

Approximation Schemes in Logic and Artificial Intelligence

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Part of the book series: Lecture Notes in Computer Science ((TRS,volume 5390))

Abstract

Approximate reasoning is used in a variety of reasoning tasks in logic-based artificial intelligence. In this paper we present several such reasoning schemes and show how they relate and differ from the approach of Pawlak’s Rough Sets.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Kentucky, Lexington, KY 40506-0046, USA

    Victor W. Marek & Mirosław Truszczyński

Authors
  1. Victor W. Marek
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  2. Mirosław Truszczyński
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Manitoba, R3T 5V6, Winnipeg, Manitoba, Canada

    James F. Peters

  2. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

  3. Institute of Computer Science, Warsaw University of Technology, Nowowoiejska 15/19, 00-665, Warsaw, Poland

    Henryk Rybiński

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Marek, V.W., Truszczyński, M. (2008). Approximation Schemes in Logic and Artificial Intelligence. In: Peters, J.F., Skowron, A., Rybiński, H. (eds) Transactions on Rough Sets IX. Lecture Notes in Computer Science, vol 5390. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89876-4_8

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  • DOI: https://doi.org/10.1007/978-3-540-89876-4_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89875-7

  • Online ISBN: 978-3-540-89876-4

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