Learning Programs as Logical Queries

Program learning focuses on the automatic generation of programs satisfying the goal of a teacher. One common approach is counter-example guided inductive synthesis, where we generate a sequence of candidate programs and the teacher responds with counter-examples for which the candidate fails. In this paper we focus on a logical approach, where programs are tuples of logical formulas, i.e. logical queries, and inputs and outputs are relational structures. We introduce our model of inductive synthesis and our implementation of it using SAT and QBF solvers. We survey basic theoretical properties of our model and show a few experimental results: learning complexity-theoretic reductions, polynomial-time programs, and learning board games from examples.