Premise Selection for Mathematics by Corpus Analysis and Kernel Methods

Smart premise selection is essential when using automated reasoning as a tool for large-theory formal proof development. This work develops learning-based premise selection in two ways. First, a fine-grained dependency analysis of existing high-level formal mathematical proofs is used to build a lar...

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Veröffentlicht in:	Journal of automated reasoning Jg. 52; H. 2; S. 191 - 213
Hauptverfasser:	Alama, Jesse, Heskes, Tom, Kühlwein, Daniel, Tsivtsivadze, Evgeni, Urban, Josef
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer Netherlands 01.02.2014 Springer Springer Nature B.V
Schlagworte:	Algorithms Applied sciences Artificial Intelligence Automated reasoning Benchmarking Computer Science Computer science; control theory; systems Construction Data processing. List processing. Character string processing Exact sciences and technology Kernels Knowledge bases (artificial intelligence) Learning and adaptive systems Logic and foundations Mathematical analysis Mathematical Logic and Formal Languages Mathematical Logic and Foundations Mathematical logic, foundations, set theory Mathematics Memory organisation. Data processing Proof theory and constructive mathematics Proving Sciences and techniques of general use Software Symbolic and Algebraic Manipulation Automated reasoning in large theories Premise selection Automated theorem proving Machine learning Automated reasoning Formal method Knowledge base Automatic proving Kernel method Theorem proving Fine grain structure Proof theory Artificial intelligence Formal verification
ISSN:	0168-7433, 1573-0670
Online-Zugang:	Volltext
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