Monadic memoization towards correctness-preserving reduction of search

Authors:
Richard Frost
Affiliations:
School of Computer Science, University of Windsor, Ontario, Canada
Venue:
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Year:
2003

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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.