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Monadic Memoization towards Correctness-Preserving Reduction of Search

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Advances in Artificial Intelligence (Canadian AI 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2671))

Abstract

Memoization is a well-known method which makes use of a table of previously-computed results in order to ensure that parts of a search (or computation)s pace are not revisited. A new technique is presented which enables the systematic and selective memoization of a wide range of algorithms. The technique overcomes disadvantages of previous approaches. In particular, the proposed technique can help programmers avoid mistakes that can result in sub-optimal use of memoization. In addition, the resulting memoized programs are amenable to analysis using equational reasoning. It is anticipated that further work will lead to proof of correctness of the proposed memoization technique.

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Author information

Authors and Affiliations

  1. School of Computer Science, University of Windsor, Ontario, Canada

    Richard Frost

Authors
  1. Richard Frost
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Editor information

Editors and Affiliations

  1. Department of Computing and Information Science, College of Physical and Engineering Science, University of Guelph, Guelph, Ontario, Canada, N1G 2W1

    Yang Xiang

  2. Dépt. Informatique-Génie Logiciel, Université Laval, Pavillon Pouliot, Ste-Foy, PQ, Canada, G1K 7P4

    Brahim Chaib-draa

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© 2003 Springer-Verlag Berlin Heidelberg

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Frost, R. (2003). Monadic Memoization towards Correctness-Preserving Reduction of Search. In: Xiang, Y., Chaib-draa, B. (eds) Advances in Artificial Intelligence. Canadian AI 2003. Lecture Notes in Computer Science, vol 2671. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44886-1_8

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  • DOI: https://doi.org/10.1007/3-540-44886-1_8

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40300-5

  • Online ISBN: 978-3-540-44886-0

  • eBook Packages: Springer Book Archive

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