Decision Trees for Binary Subword-Closed Languages

In this paper, we study arbitrary subword-closed languages over the alphabet [Formula: see text] (binary subword-closed languages). For the set of words [Formula: see text] of the length n belonging to a binary subword-closed language L, we investigate the depth of the decision trees solving the recognition and the membership problems deterministically and nondeterministically. In the case of the recognition problem, for a given word from [Formula: see text] , we should recognize it using queries, each of which, for some [Formula: see text] , returns the ith letter of the word. In the case of the membership problem, for a given word over the alphabet [Formula: see text] of the length n, we should recognize if it belongs to the set [Formula: see text] using the same queries. With the growth of n, the minimum depth of the decision trees solving the problem of recognition deterministically is either bounded from above by a constant or grows as a logarithm, or linearly. For other types of trees and problems (decision trees solving the problem of recognition nondeterministically and decision trees solving the membership problem deterministically and nondeterministically), with the growth of n, the minimum depth of the decision trees is either bounded from above by a constant or grows linearly. We study the joint behavior of the minimum depths of the considered four types of decision trees and describe five complexity classes of binary subword-closed languages.