A Combined Analytical and Search-Based Approach for the Inductive Synthesis of Functional Programs

Abstract

Inductive program synthesis addresses the problem of automatically generating (declarative) recursive programs from ambiguous specifications such as input/output examples. Potential applications range from software development to intelligent agents that learn in recursive domains. Current systems suffer from either strong restrictions regarding the form of inducible programs or from blind search in vast program spaces. The main contribution of my dissertation (Kitzelmann, Ph.D. thesis, 2010) is the algorithm Igor2 for the induction of functional programs. It is based on search in program spaces but derives candidate programs directly from examples, rather than using them as test cases, and thereby prunes many programs. Experiments show promising results.