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Non-leftmost Unfolding in Partial Evaluation of Logic Programs with Impure Predicates

  • Elvira Albert
  • Germán Puebla
  • John P. Gallagher
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3901)

Abstract

Partial evaluation of logic programs which contain impure predicates poses non-trivial challenges. Impure predicates include those which produce side-effects, raise errors (or exceptions), and those whose truth value varies according to the degree of instantiation of arguments. In particular, non-leftmost unfolding steps can produce incorrect results since the independence of the computation rule no longer holds in the presence of impure predicates. Existing proposals allow non-leftmost unfolding steps, but at the cost of accuracy: bindings and failure are not propagated backwards to predicates which are potentially impure. In this work we propose a partial evaluation scheme which substantially reduces the situations in which such backpropagation has to be avoided. With this aim, our partial evaluator takes into account the information about purity of predicates expressed in terms of assertions. This allows some optimizations which are not feasible using existing partial evaluation techniques. We argue that our proposal goes beyond existing ones in that it is a) accurate, since the classification of pure vs impure is done at the level of atoms instead of predicates, b) extensible, as the information about purity can be added to programs using assertions without having to modify the partial evaluator itself, and c) automatic, since (backwards) analysis can be used to automatically infer the required assertions. Our approach has been implemented in the context of CiaoPP, the abstract interpretation-based preprocessor of the Ciao logic programming system.

Keywords

Logic Program Partial Evaluation Derivation Step Computation Rule Partial Evaluator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Elvira Albert
    • 1
  • Germán Puebla
    • 2
  • John P. Gallagher
    • 3
  1. 1.School of Computer ScienceComplutense U. of MadridSpain
  2. 2.School of Computer ScienceTechnical U. of MadridSpain
  3. 3.Department of Computer ScienceUniversity of RoskildeDenmark

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