Automatic Learning in Proof Planning

In this paper we present a framework for automated learning within mathematical reasoning systems. In particular, this framework enables proof planning systems to automatically learn new proof methods from well chosen examples of proofs which use a similar reasoning pattern to prove related theorems. Our framework consists of a representation formalism for methods and a machine learning technique which can learn methods using this representation formalism. We present the implementation of this framework within the mega proof planning system, and some experiments we ran on this implementation to evaluate the validity of our approach