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ILP through propositionalization and stochastic k-term DNF learning

Publication ,  Conference
Paes, A; Železný, F; Zaverucha, G; Page, D; Srinivasan, A
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 2007
Published version (DOI)

One promising family of search strategies to alleviate runtime and storage requirements of ILP systems is that of stochastic local search methods, which have been successfully applied to hard propositional tasks such as satisfiability. Stochastic local search algorithms for propositional satisfiability benefit from the ability to quickly test whether a truth assignment satisfies a formula. Because of that many possible solutions can be tested and scored in a short time. In contrast, testing whether a clause covers an example in ILP takes much longer, so that far fewer possible solutions can be tested in the same time. Therefore in this paper we investigate stochastic local search in ILP using a relational propositionalized problem instead of directly use the first-order clauses space of solutions. © Springer-Verlag Berlin Heidelberg 2007.

Duke Scholars

David Page
Author David Page Biostatistics & Bioinformatics, Division of Biostatistics

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

10.1007/978-3-540-73847-3_35

EISSN

1611-3349

ISSN

0302-9743

Publication Date

January 1, 2007

Volume

4455 LNAI

Start / End Page

379 / 393

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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Paes, A., Železný, F., Zaverucha, G., Page, D., & Srinivasan, A. (2007). ILP through propositionalization and stochastic k-term DNF learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4455 LNAI, pp. 379–393). https://doi.org/10.1007/978-3-540-73847-3_35
Paes, A., F. Železný, G. Zaverucha, D. Page, and A. Srinivasan. “ILP through propositionalization and stochastic k-term DNF learning.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4455 LNAI:379–93, 2007. https://doi.org/10.1007/978-3-540-73847-3_35.
Paes A, Železný F, Zaverucha G, Page D, Srinivasan A. ILP through propositionalization and stochastic k-term DNF learning. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2007. p. 379–93.
Paes, A., et al. “ILP through propositionalization and stochastic k-term DNF learning.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4455 LNAI, 2007, pp. 379–93. Scopus, doi:10.1007/978-3-540-73847-3_35.
Paes A, Železný F, Zaverucha G, Page D, Srinivasan A. ILP through propositionalization and stochastic k-term DNF learning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2007. p. 379–393.

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

10.1007/978-3-540-73847-3_35

EISSN

1611-3349

ISSN

0302-9743

Publication Date

January 1, 2007

Volume

4455 LNAI

Start / End Page

379 / 393

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences