Classical learning algorithms for Boolean functions assume that unknown targets are Boolean functions over fixed variables. The assumption precludes scenarios where indefinitely many variables are needed. It also induces unnecessary queries when many variables are redundant. Based on a classical learning algorithm for Boolean functions, we develop two learning algorithms to infer Boolean functions over enlarging sets of ordered variables. We evaluate their performance in the learning-based loop invariant generation framework. © 2012 Springer-Verlag.
CITATION STYLE
Chen, Y. F., & Wang, B. Y. (2012). Learning Boolean functions incrementally. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7358 LNCS, pp. 55–70). https://doi.org/10.1007/978-3-642-31424-7_10
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