An efficient query learning algorithm for ordered binary decision diagrams

Abstract

In this paper, we propose a new algorithm that exactly learns ordered binary decision diagrams (OBDDs) with a given variable ordering via equivalence and membership queries. Our algorithm uses at most n equivalence queries and at most 2 n (⌈log 2 m ⌉ + 3 n ) membership queries, where n is the number of nodes in the target-reduced OBDD and m is the number of variables. The upper bound on the number of membership queries is smaller by a factor of O(m) compared with that for the previous best known algorithm proposed by [R. Gavaldà, D. Guijarro, Learning Ordered Binary Decision Diagrams, Proceedings of the 6th International Workshop on Algorithmic Learning Theory , 1995, pp. 228–238].