An efficient query learning algorithm for zerosuppressed binary decision diagrams
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Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:360371, 2017.
Abstract
A ZDD is a directed acyclic graph that represents a family of sets over a fixed universe set. In this paper, we propose an algorithm that learns zerosuppressed binary decision diagrams (ZDDs) using membership and equivalence queries. If the target ZDD has $n$ nodes and the cardinality of the universe is $m$, our algorithm uses $n$ equivalence queries and at most $n(\lfloor \log m \rfloor + 4n)$ membership queries to learn the target ZDD.
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