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European Workshop on Logics in Artificial Intelligence

JELIA 2014: Logics in Artificial Intelligence pp 166–180Cite as

Improving the Normalization of Weight Rules in Answer Set Programs

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8761))

Abstract

Cardinality and weight rules are important primitives in answer set programming. In this context, normalization means the translation of such rules back into normal rules, e.g., for the sake of boosting the search for answers sets. For instance, the normalization of cardinality rules can be based on Boolean circuits that effectively sort or select greatest elements amongst Boolean values. In this paper, we develop further constructions for the normalization of weight rules and adapt techniques that have been previously used to translate pseudo-Boolean constraints into the propositional satisfiability (SAT) problem. In particular, we consider mixed-radix numbers as an efficient way to represent and encode integer weights involved in a weight rule and propose a heuristic for selecting a suitable base. Moreover, we incorporate a scheme for structure sharing in the normalization procedure. In the experimental part, we study the effect of normalizing weight rules on compactness and search performance measured in terms of program size, search time, and number of conflicts.

The support from the Finnish Centre of Excellence in Computational Inference Research (COIN) funded by the Academy of Finland (under grant #251170) is gratefully acknowledged.

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Authors and Affiliations

  1. Helsinki Institute for Information Technology HIIT Department of Information and Computer Science, Aalto University, FI-00076, AALTO, Finland

    Jori Bomanson, Martin Gebser & Tomi Janhunen

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  1. Jori Bomanson
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  2. Martin Gebser
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  3. Tomi Janhunen
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Editors and Affiliations

  1. University of Madeira, Funchal, Madeira, Portugal

    Eduardo Fermé

  2. CENTRIA, Universidade Nova de Lisboa, Caparica, Portugal

    João Leite

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Bomanson, J., Gebser, M., Janhunen, T. (2014). Improving the Normalization of Weight Rules in Answer Set Programs. In: Fermé, E., Leite, J. (eds) Logics in Artificial Intelligence. JELIA 2014. Lecture Notes in Computer Science(), vol 8761. Springer, Cham. https://doi.org/10.1007/978-3-319-11558-0_12

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  • DOI: https://doi.org/10.1007/978-3-319-11558-0_12

  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-11558-0

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