A Dichotomy Theorem for Learning Quantified Boolean Formulas
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 and 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
Text
Dalmau. "A Dichotomy Theorem for Learning Quantified Boolean Formulas." Machine Learning, 1999. doi:10.1023/A:1007582729656Markdown
[Dalmau. "A Dichotomy Theorem for Learning Quantified Boolean Formulas." Machine Learning, 1999.](https://mlanthology.org/mlj/1999/dalmau1999mlj-dichotomy/) doi:10.1023/A:1007582729656BibTeX
@article{dalmau1999mlj-dichotomy,
title = {{A Dichotomy Theorem for Learning Quantified Boolean Formulas}},
author = {Dalmau, Víctor},
journal = {Machine Learning},
year = {1999},
pages = {207-224},
doi = {10.1023/A:1007582729656},
volume = {35},
url = {https://mlanthology.org/mlj/1999/dalmau1999mlj-dichotomy/}
}