MULTI-OUTPUT RANKING FOR AUTOMATED REASONING

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

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Paper

MULTI-OUTPUT RANKING FOR AUTOMATED REASONING

Topics: Machine Learning; Mining High-Dimensional Data; Structured Data Analysis and Statistical Methods

In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 0IC3K, 42-51, 2011 , Paris, France

Authors: Daniel Kühlwein ; Josef Urban ; Evgeni Tsivtsivadze ; Herman Geuvers and Tom Heskes

Affiliation: Radboud University Nijmegen, Netherlands

Keyword(s): Ranking, Automated theorem proving, Premise selection, Machine learning.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Mining High-Dimensional Data ; Soft Computing ; Structured Data Analysis and Statistical Methods ; Symbolic Systems

Abstract: 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 several formats:

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 (IC3K 2011) - KDIR; ISBN 978-989-8425-79-9; ISSN 2184-3228, SciTePress, pages 42-51. DOI: 10.5220/0003650400420051

@conference{kdir11,
author={Daniel Kühlwein. and Josef Urban. and Evgeni Tsivtsivadze. and Herman Geuvers. and Tom Heskes.},
title={MULTI-OUTPUT RANKING FOR AUTOMATED REASONING},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (IC3K 2011) - KDIR},
year={2011},
pages={42-51},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003650400420051},
isbn={978-989-8425-79-9},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (IC3K 2011) - KDIR
TI - MULTI-OUTPUT RANKING FOR AUTOMATED REASONING
SN - 978-989-8425-79-9
IS - 2184-3228
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
PB - SciTePress