A Dichotomy Theorem for Learning Quantified Boolean Formulas

作者:Víictor Dalmau

摘要

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.

论文关键词:computational learning theory, qualified boolean formulas, dichotomy theorems

论文评审过程:

论文官网地址:https://doi.org/10.1023/A:1007582729656