Dichotomy theorem for learning quantified boolean formulas

9Citations
Citations of this article
24Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

We consider the following classes of quantified boolean formulas. Fix a finite set of basic boolean functions. Take conjunctions of these basic functions applied to variables constants in arbitrary ways. Finally quantify existentially or universally some of the variables. We prove the following dichotomy theorem: For any set of basic boolean functions, the resulting set of formulas is either polynomially learnable from equivalence queries alone or else it is not PAC-predictable even with membership queries under cryptographic assumptions. Furthermore, we identify precisely which sets of basic functions are in which of the two cases.

Cite

CITATION STYLE

APA

Dalmau, V. (1999). Dichotomy theorem for learning quantified boolean formulas. Machine Learning, 35(3), 207–224. https://doi.org/10.1023/A:1007582729656

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free