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Algorithm Selection for Paracoherent Answer Set Computation

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Logics in Artificial Intelligence (JELIA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11468))

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Abstract

Answer Set Programming (ASP) is a well-established AI formalism rooted in nonmonotonic reasoning. Paracoherent semantics for ASP have been proposed to derive useful conclusions also in the absence of answer sets caused by cyclic default negation. Recently, several different algorithms have been proposed to implement them, but no algorithm is always preferable to the others in all instances. In this paper, we apply algorithm selection techniques to devise a more efficient paracoherent answer set solver combining existing algorithms. The effectiveness of the approach is demonstrated empirically running our system on existing benchmarks.

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Acknowledgments

This work has been supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant agreement No. 690974 for the project “MIREL: MIning and REasoning with Legal texts”.

Author information

Authors and Affiliations

  1. University of Calabria, Rende, Italy

    Giovanni Amendola & Francesco Ricca

  2. University of Genoa, Genoa, Italy

    Carmine Dodaro

  3. University of Klagenfurt, Klagenfurt, Austria

    Wolfgang Faber

  4. University of Sassari, Sassari, Italy

    Luca Pulina

Authors
  1. Giovanni Amendola
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  2. Carmine Dodaro
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  3. Wolfgang Faber
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  4. Luca Pulina
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  5. Francesco Ricca
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Corresponding author

Correspondence to Giovanni Amendola .

Editor information

Editors and Affiliations

  1. University of Calabria, Rende, Italy

    Francesco Calimeri

  2. University of Calabria, Rende, Italy

    Nicola Leone

  3. Mathematics and Computer Science, University of Calabria, Rende, Italy

    Marco Manna

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Amendola, G., Dodaro, C., Faber, W., Pulina, L., Ricca, F. (2019). Algorithm Selection for Paracoherent Answer Set Computation. In: Calimeri, F., Leone, N., Manna, M. (eds) Logics in Artificial Intelligence. JELIA 2019. Lecture Notes in Computer Science(), vol 11468. Springer, Cham. https://doi.org/10.1007/978-3-030-19570-0_31

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  • DOI: https://doi.org/10.1007/978-3-030-19570-0_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-19569-4

  • Online ISBN: 978-3-030-19570-0

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