TY - JOUR
AU - Mateja Jamnik, Manfred Kerber, Martin Pollet, Christoph Benzmüller
AB - In this paper we present an approach to automated learning within mathematical reasoning systems. In particular, the approach 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 approach consists of an abstract representation for methods and a machine learning technique which can learn methods using this representation formalism. We present an implementation of the approach within the Ω mega proof planning system, which we call L earn Ω matic . We also present the results of the experiments that we ran on this implementation in order to evaluate if and how it improves the power of proof planning systems. Key words
TI - Automatic Learning of Proof Methods in Proof Planning
JF - Logic Journal of the IGPL
DO - 10.1093/jigpal/11.6.647
DA - 2003-11-01
UR - https://www.deepdyve.com/lp/oxford-university-press/automatic-learning-of-proof-methods-in-proof-planning-WRxNpM7UW0
SP - 647
VL - 11
IS - 6
DP - DeepDyve
ER -