Adaptive Versus Nonadaptive Attribute-Efficient Learning

TLDR: A graph-theoretic characterization of nonadaptive learning families, called r-wise bipartite connected families, are given and it is proved that the optimal query number O(2r + r log n) can be already achieved in O(r) stages.

Abstract: We study the complexity of learning arbitrary Boolean functions of n variables by membership queries, if at most r variables are relevant. Problems of this type have important applications in fault searching, e.g. logical circuit testing and generalized group testing. Previous literature concentrates on special classes of such Boolean functions and considers only adaptive strategies. First we give a straightforward adaptive algorithm using O(r2r log n) queries, but actually, most queries are asked nonadaptively. This leads to the problem of purely nonadaptive learning. We give a graph-theoretic characterization of nonadaptive learning families, called r-wise bipartite connected families. By the probabilistic method we show the existence of such families of size O(r2r log n + r22r). This implies that nonadaptive attribute-efficient learning is not essentially more expensive than adaptive learning. We also sketch an explicit pseudopolynomial construction, though with a slightly worse bound. It uses the common derandomization technique of small-biased k-independent sample spaces. For the special case r e 2, we get roughly 2.275 log n adaptive queries, which is fairly close to the obvious lower bound of 2 log n. For the class of monotone functions, we prove that the optimal query number O(2r + r log n) can be already achieved in O(r) stages. On the other hand, Ω(2r log n) is a lower bound on nonadaptive queries.