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Integrating Induction and Coinduction via Closure Operators and Proof Cycles

Coinductive reasoning about infinitary data structures has many applications in computer science. Nonetheless developing natural proof systems (especially ones amenable to automation) for reasoning about coinductive data remains a challenge. This paper presents a minimal, generic formal framework th...

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Detalles Bibliográficos
Autores principales: Cohen, Liron, Rowe, Reuben N. S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7324239/
http://dx.doi.org/10.1007/978-3-030-51074-9_21
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author Cohen, Liron
Rowe, Reuben N. S.
author_facet Cohen, Liron
Rowe, Reuben N. S.
author_sort Cohen, Liron
collection PubMed
description Coinductive reasoning about infinitary data structures has many applications in computer science. Nonetheless developing natural proof systems (especially ones amenable to automation) for reasoning about coinductive data remains a challenge. This paper presents a minimal, generic formal framework that uniformly captures applicable (i.e. finitary) forms of inductive and coinductive reasoning in an intuitive manner. The logic extends transitive closure logic, a general purpose logic for inductive reasoning based on the transitive closure operator, with a dual ‘co-closure’ operator that similarly captures applicable coinductive reasoning in a natural, effective manner. We develop a sound and complete non-well-founded proof system for the extended logic, whose cyclic subsystem provides the basis for an effective system for automated inductive and coinductive reasoning. To demonstrate the adequacy of the framework we show that it captures the canonical coinductive data type: streams.
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spelling pubmed-73242392020-06-30 Integrating Induction and Coinduction via Closure Operators and Proof Cycles Cohen, Liron Rowe, Reuben N. S. Automated Reasoning Article Coinductive reasoning about infinitary data structures has many applications in computer science. Nonetheless developing natural proof systems (especially ones amenable to automation) for reasoning about coinductive data remains a challenge. This paper presents a minimal, generic formal framework that uniformly captures applicable (i.e. finitary) forms of inductive and coinductive reasoning in an intuitive manner. The logic extends transitive closure logic, a general purpose logic for inductive reasoning based on the transitive closure operator, with a dual ‘co-closure’ operator that similarly captures applicable coinductive reasoning in a natural, effective manner. We develop a sound and complete non-well-founded proof system for the extended logic, whose cyclic subsystem provides the basis for an effective system for automated inductive and coinductive reasoning. To demonstrate the adequacy of the framework we show that it captures the canonical coinductive data type: streams. 2020-05-30 /pmc/articles/PMC7324239/ http://dx.doi.org/10.1007/978-3-030-51074-9_21 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Cohen, Liron
Rowe, Reuben N. S.
Integrating Induction and Coinduction via Closure Operators and Proof Cycles
title Integrating Induction and Coinduction via Closure Operators and Proof Cycles
title_full Integrating Induction and Coinduction via Closure Operators and Proof Cycles
title_fullStr Integrating Induction and Coinduction via Closure Operators and Proof Cycles
title_full_unstemmed Integrating Induction and Coinduction via Closure Operators and Proof Cycles
title_short Integrating Induction and Coinduction via Closure Operators and Proof Cycles
title_sort integrating induction and coinduction via closure operators and proof cycles
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7324239/
http://dx.doi.org/10.1007/978-3-030-51074-9_21
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