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An efficient query learning algorithm for ordered binary decision diagrams

机译:一种有效的有序二元决策图查询学习算法

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摘要

In this paper, we propose a new algorithm that exactly learns ordered binary decision diagrams (OB-DDs) with a given variable ordering via equivalence and membership queries. Our algorithm uses at most n equivalence queries and at most 2n([log_2 m] + 3n) 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. Gavalda and D. Guijarro [Learning Ordered Binary Decision Diagrams, Proceedings of the 6th International Workshop on Algorithmic Learning Theory, 1995, pp. 228-238].
机译:在本文中,我们提出了一种新算法,该算法可通过对等和隶属关系查询来精确学习具有给定变量排序的有序二进制决策图(OB-DD)。我们的算法最多使用n个等价查询和最多2n([log_2 m] + 3n)个成员资格查询,其中n是目标约简OBDD中的节点数,m是变量数。与先前由R. Gavalda和D. Guijarro [第六届国际研讨会论文集学习有序二元决策图]提出的最著名算法相比,成员资格查询数量的上限小O(m)。算法学习理论,1995年,第228-238页]。

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