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AbstractAbstract. In this paper we present a framework for automatedlearning within mathematical reasoning systems. In particular, this framework enables proof planning systems to automatically learnnew proof methods from well chosen examples of proofs which use a similar reasoning pattern to prove related theorems. Our frameworkconsists of a representation formalism for methods and a machine learning technique which can learn methods using this representationformalism. We present the implementation of this framework within the \Omega MEGA proof planning system, and some experiments we ran onthis implementation to evaluate the validity of our approach.