Minimizing the average query complexity of learning monotone Boolean functions

This paper addresses the problem of completely reconstructing deterministic monotone Boolean functions via membership queries. The minimum average query complexity is guaranteed via recursion, where partially ordered sets (posets) make up the overlapping subproblems. For problems with up to 4 variables, the posets' optimality conditions are summarized in the form of an evaluative criterion. The evaluative criterion extends the computational feasibility to problems involving up to about 20 variables. A framework for unbiased average case comparison of monotone Boolean function inference algorithms is developed using unequal probability sampling. The unbiased empirical results show that an implementation of the subroutine considered as a standard in the literature performs almost twice as many queries as the evaluative criterion on the average. It should also be noted that the first algorithm ever designed for this problem performed consistently within two percentage points of the evaluative criterion. As such, it prevails, by far, as the most efficient of the many preexisting algorithms.