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Relational Learning: Hard Problems and Phase Transitions

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Book cover AI*IA 99: Advances in Artificial Intelligence (AI*IA 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1792))

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Abstract

This paper focuses on a major step of machine learning, namely checking whether an example matches a candidate hypothesis. In relational learning, matching can be viewed as a Constraint Satisfaction Problem (CSP). The complexity of the task is analyzed in the Phase Transition framework, investigating the impact on the effectiveness of two relational learners: FOIL and G-NET.

The critical factors of complexity, and their critical values, are experimentally investigated on artificial problems. This leads to distinguish several kinds of learning domains, depending on whether the target concept lies in the “mushy” region or not. Interestingly, experiments done with FOIL and G-NET show that both learners tend to induce hypotheses generating matching problems located inside the phase transition region, even if the constructed target concept lies far outside. Moreover, target concepts constructed too close to the phase transition are hard and both learners fail. The paper offers an explanation for this fact, and proposes a classification of learning domains and their hardness.

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Author information

Authors and Affiliations

  1. Dipartimento di Informatica, Università di Torino, Corso Svizzera 185, 10149, Torino, Italy

    M. Botta

  2. Dipartimento di Scienze e Tecnologie Avanzate, Università del Piemonte Orientale “Amedeo Avogadro”, Corso Borsalino 54, 15100, Alessandria, Italy

    A. Giordana & L. Saitta

  3. Laboratoire de Méchanique des Solides, Ecole Polytechnique, 91128, Palaiseau, France

    M. Sebag

Authors
  1. M. Botta
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  2. A. Giordana
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  3. L. Saitta
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  4. M. Sebag
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Editor information

Editors and Affiliations

  1. DEIS, University of Bologna, Viale Risorgimento, 2, 40136, Bologna, Italy

    Evelina Lamma  & Paola Mello  & 

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© 2000 Springer-Verlag Berlin Heidelberg

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Botta, M., Giordana, A., Saitta, L., Sebag, M. (2000). Relational Learning: Hard Problems and Phase Transitions. In: Lamma, E., Mello, P. (eds) AI*IA 99: Advances in Artificial Intelligence. AI*IA 1999. Lecture Notes in Computer Science(), vol 1792. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46238-4_16

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  • DOI: https://doi.org/10.1007/3-540-46238-4_16

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67350-7

  • Online ISBN: 978-3-540-46238-5

  • eBook Packages: Springer Book Archive

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