Daniel Kühlwein, Josef Urban, Evgeni Tsivtsivadze, Herman Geuvers, Tom Heskes


Premise selection and ranking is a pressing problem for applications of automated reasoning to large formal theories and knowledge bases. Smart selection of premises has a significant impact on the efficiency of automated proof assistant systems in large theories. Despite this, machine-learning methods for this domain are underdeveloped. In this paper we propose a general learning algorithm to address the premise selection problem. Our approach consists of simultaneous training of multiple predictors that learn to rank a set of premises in order to estimate their expected usefulness when proving a new conjecture. The proposed algorithm efficiently constructs prediction functions and can take correlations among multiple tasks into account. The experiments demonstrate that the proposed method significantly outperforms algorithms previously applied to the task.


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Paper Citation

in Harvard Style

Kühlwein D., Urban J., Tsivtsivadze E., Geuvers H. and Heskes T. (2011). MULTI-OUTPUT RANKING FOR AUTOMATED REASONING . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011) ISBN 978-989-8425-79-9, pages 42-51. DOI: 10.5220/0003650400420051

in Bibtex Style

author={Daniel Kühlwein and Josef Urban and Evgeni Tsivtsivadze and Herman Geuvers and Tom Heskes},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011)},

in EndNote Style

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011)
SN - 978-989-8425-79-9
AU - Kühlwein D.
AU - Urban J.
AU - Tsivtsivadze E.
AU - Geuvers H.
AU - Heskes T.
PY - 2011
SP - 42
EP - 51
DO - 10.5220/0003650400420051