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A Model+Solver Approach to Concept Learning

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AI*IA 2016 Advances in Artificial Intelligence (AI*IA 2016)

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Abstract

Many Concept Learning problems can be seen as Constraint Satisfaction Problems (CSP). In this paper, we propose a model+solver approach to Concept Learning which combines the efficacy of Description Logics (DLs) in conceptual modeling with the efficiency of Answer Set Programming (ASP) solvers in dealing with CSPs.

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Notes

  1. 1.

    http://www.w3.org/TR/2009/REC-owl2-overview-20091027/.

  2. 2.

    http://archive.ics.uci.edu/ml/datasets/Trains.

  3. 3.

    http://dl-learner.org/Projects/DLLearner.

  4. 4.

    https://github.com/wolpertinger-reasoner/.

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Acknowledgements

We would like to thank the proposers of the bounded model semantics for the fruitful discussions about their work during a visit to Dresden and for the kind remote assistance in using Wolpertinger.

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Authors and Affiliations

  1. Dipartimento di Informatica, Università degli Studi di Bari “Aldo Moro”, Bari, Italy

    Francesca Alessandra Lisi

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  1. Francesca Alessandra Lisi
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Correspondence to Francesca Alessandra Lisi .

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Editors and Affiliations

  1. University of Genoa , Genova, Italy

    Giovanni Adorni

  2. University of Parma , Parma, Italy

    Stefano Cagnoni

  3. University of Siena , Siena, Italy

    Marco Gori

  4. University of Genova , Genova, Italy

    Marco Maratea

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Lisi, F.A. (2016). A Model+Solver Approach to Concept Learning. In: Adorni, G., Cagnoni, S., Gori, M., Maratea, M. (eds) AI*IA 2016 Advances in Artificial Intelligence. AI*IA 2016. Lecture Notes in Computer Science(), vol 10037. Springer, Cham. https://doi.org/10.1007/978-3-319-49130-1_20

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  • DOI: https://doi.org/10.1007/978-3-319-49130-1_20

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  • Print ISBN: 978-3-319-49129-5

  • Online ISBN: 978-3-319-49130-1

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