In this paper the authors describe an interior point mathematical programming approach to inductive inference. They list several versions of this problem and study in detail the formulation based on hidden Boolean logic. The authors consider the problem of identifying a hidden Boolean function ℱ:{0,1}n⇒{0,1ℝ using outputs obtained by applying a limited number of random inputs to the hidden function. Given this input-output sample, they give a method to synthesize a Boolean function that describes the sample. The authors pose the Boolean Function Synthesis Problem as a particular type of Satisfiability Problem. The Satisfiability Problem is translated into an integer programming feasibility problem, that is solved with an interior point algorithm for integer programming. A similar integer programming implementation has been used in a previous study to solve randomly generated instances of the Satisfiability Problem. In this paper the authors introduce a new variant of this algorithm, where the Riemannian metric used for defining the search region is dynamically modified. Computational results on 8-, 16- and 32-input, 1-output functions are presented. The present implementation successfully identified the majority of hidden functions in the experiment.
