Machine learning coalgebraic proofs

Ekaterina Komendantskaya

doi:10.1142/9781783265091_0020

Machine learning coalgebraic proofs

Ekaterina Komendantskaya

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Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

This chapter argues for a novel method to machine learn patterns in formal proofs using statistical machine learning methods. The method exploits coalgebraic approach to proofs. The success of the method is demonstrated on three applications allowing to distinguish well-formed proofs from ill-formed proofs, identify families of proofs and even families of potentially provable goals.

Original language	English
Title of host publication	Latest Advances in Inductive Logic Programming
Publisher	Imperial College Press
Pages	191-198
Number of pages	8
ISBN (Electronic)	9781783265091
ISBN (Print)	9781783265084
DOIs	https://doi.org/10.1142/9781783265091_0020
Publication status	Published - Dec 2014

ASJC Scopus subject areas

General Computer Science

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10.1142/9781783265091_0020

Cite this

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title = "Machine learning coalgebraic proofs",

abstract = "This chapter argues for a novel method to machine learn patterns in formal proofs using statistical machine learning methods. The method exploits coalgebraic approach to proofs. The success of the method is demonstrated on three applications allowing to distinguish well-formed proofs from ill-formed proofs, identify families of proofs and even families of potentially provable goals.",

author = "Ekaterina Komendantskaya",

note = "The work was supported by the Engineering and Physical Sciences Research Council, UK; Postdoctoral Fellow research grant EP/F044046/2. ",

year = "2014",

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AB - This chapter argues for a novel method to machine learn patterns in formal proofs using statistical machine learning methods. The method exploits coalgebraic approach to proofs. The success of the method is demonstrated on three applications allowing to distinguish well-formed proofs from ill-formed proofs, identify families of proofs and even families of potentially provable goals.

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Machine learning coalgebraic proofs

Abstract

ASJC Scopus subject areas

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Coalgebraic Logic Programming for Type Inference: Parallelism and Corecursion for New Generation of Programming Languages (Joint with the University of Bath)

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Machine learning coalgebraic proofs

Abstract

ASJC Scopus subject areas

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Coalgebraic Logic Programming for Type Inference: Parallelism and Corecursion for New Generation of Programming Languages (Joint with the University of Bath)

Cite this