Towards learning new methods in proof planning

Abstract. In this paper we propose how proof planning systems can be extended by an automated learning capability. The idea is that a proof planner would be capable of learning new proof methods from well chosen examples of proofs which use a similar reasoning strategy to prove related theorems, and this strategy could be characterised as a proof method. We propose a representation framework for methods, and a machine learning technique which can learn methods using this representation framework. The technique can be applied (amongst other) to learn whether and when to call external systems such as theorem provers or computer algebra systems. This is work in progress, and we hope to gain useful feedback from the CALCULEMUS community.