Induction as Consequence Finding

Abstract

This paper presents a general procedure for inverse entailment which constructs inductive hypotheses in inductive logic programming. Based on inverse entailment, not only unit clauses but also characteristic clauses are deduced from a background theory together with the negation of positive examples. Such clauses can be computed by a resolution method for consequence finding. Unlike previous work on inverse entailment, our proposed method called CF-induction is sound and complete for finding hypotheses from full clausal theories, and can be used for inducing not only definite clauses but also non-Horn clauses and integrity constraints. We also show that CF-induction can be used to compute abductive explanations, and then compare induction and abduction from the viewpoint of inverse entailment and consequence finding.

Cite

Text

Inoue. "Induction as Consequence Finding." Machine Learning, 2004. doi:10.1023/B:MACH.0000023149.72125.E2

Markdown

[Inoue. "Induction as Consequence Finding." Machine Learning, 2004.](https://mlanthology.org/mlj/2004/inoue2004mlj-induction/) doi:10.1023/B:MACH.0000023149.72125.E2

BibTeX

@article{inoue2004mlj-induction,
  title     = {{Induction as Consequence Finding}},
  author    = {Inoue, Katsumi},
  journal   = {Machine Learning},
  year      = {2004},
  pages     = {109-135},
  doi       = {10.1023/B:MACH.0000023149.72125.E2},
  volume    = {55},
  url       = {https://mlanthology.org/mlj/2004/inoue2004mlj-induction/}
}