Abstract
ENIGMA is an efficient implementation of learning-based guidance for given clause selection in saturation-based automated theorem provers. In this work, we describe several additions to this method. This includes better clause features, adding conjecture features as the proof state characterization, better data pre-processing, and repeated model learning. The enhanced ENIGMA is evaluated on the MPTP2078 dataset, showing significant improvements.
J. Jakubův and J. Urban—This work was supported by the ERC Consolidator grant no. 649043 AI4REASON, and by the Czech project AI&Reasoning CZ.02.1.01/0.0/0.0/15_003/0000466 and the European Regional Development Fund.
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Jakubův, J., Urban, J. (2018). Enhancing ENIGMA Given Clause Guidance. In: Rabe, F., Farmer, W., Passmore, G., Youssef, A. (eds) Intelligent Computer Mathematics. CICM 2018. Lecture Notes in Computer Science(), vol 11006. Springer, Cham. https://doi.org/10.1007/978-3-319-96812-4_11
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