An approach to guided learning of boolean functions

A critical aspect in the problem of inductive inference is the number of examples needed to accurately infer a Boolean function from positive and negative examples. In this paper, we develop an approach for deriving a sequence of examples for this problem. Some computer experiments indicate that, on the average, examples derived according to the proposed approach lead to the inference of the correct function considerably faster than when examples are derived in a random order.