Pac-Learning Recursive Logic Programs: Negative Results

Abstract

In a companion paper it was shown that the class of constant-depth determinate k-ary recursive clauses is efficiently learnable. In this paper we present negative results showing that any natural generalization of this class is hard to learn in Valiant's model of pac-learnability. In particular, we show that the following program classes are cryptographically hard to learn: programs with an unbounded number of constant-depth linear recursive clauses; programs with one constant-depth determinate clause containing an unbounded number of recursive calls; and programs with one linear recursive clause of constant locality. These results immediately imply the non-learnability of any more general class of programs. We also show that learning a constant-depth determinate program with either two linear recursive clauses or one linear recursive clause and one non-recursive clause is as hard as learning boolean DNF. Together with positive results from the companion paper, these negative results establish a boundary of efficient learnability for recursive function-free clauses.

Cite

Text

Cohen. "Pac-Learning Recursive Logic Programs: Negative Results." Journal of Artificial Intelligence Research, 1995. doi:10.1613/JAIR.1917

Markdown

[Cohen. "Pac-Learning Recursive Logic Programs: Negative Results." Journal of Artificial Intelligence Research, 1995.](https://mlanthology.org/jair/1995/cohen1995jair-paclearning-a/) doi:10.1613/JAIR.1917

BibTeX

@article{cohen1995jair-paclearning-a,
  title     = {{Pac-Learning Recursive Logic Programs: Negative Results}},
  author    = {Cohen, William W.},
  journal   = {Journal of Artificial Intelligence Research},
  year      = {1995},
  pages     = {541-573},
  doi       = {10.1613/JAIR.1917},
  volume    = {2},
  url       = {https://mlanthology.org/jair/1995/cohen1995jair-paclearning-a/}
}