Logics for approximate and strong entailments

We consider two kinds of similarity-based reasoning and formalise them in a logical setting. In one case, we are led by the principle that conclusions can be drawn even if they are only approximately correct. This leads to a graded approximate entailment, which is weaker than classical entailment. I...

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Publicado: 2012
Materias:
Approximate entailment
Non-classical logics
Similarity-based reasoning
Strong entailment
Logical calculi
Non-classical logic
Artificial intelligence
Fuzzy sets
Biomineralization
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01650114_v197_n_p59_Esteva
http://hdl.handle.net/20.500.12110/paper_01650114_v197_n_p59_Esteva
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Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_01650114_v197_n_p59_Esteva
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spelling paper:paper_01650114_v197_n_p59_Esteva2023-06-08T15:14:33Z Logics for approximate and strong entailments Approximate entailment Non-classical logics Similarity-based reasoning Strong entailment Approximate entailment Logical calculi Non-classical logic Similarity-based reasoning Strong entailment Artificial intelligence Fuzzy sets Biomineralization We consider two kinds of similarity-based reasoning and formalise them in a logical setting. In one case, we are led by the principle that conclusions can be drawn even if they are only approximately correct. This leads to a graded approximate entailment, which is weaker than classical entailment. In the other case, we follow the principle that conclusions must remain correct even if the assumptions are slightly changed. This leads to a notion of a graded strong entailment, which is stronger than classical entailment. We develop two logical calculi based on the notions of approximate and of strong entailment, respectively. © 2011 Elsevier B.V. 2012 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01650114_v197_n_p59_Esteva http://hdl.handle.net/20.500.12110/paper_01650114_v197_n_p59_Esteva
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Approximate entailment
Non-classical logics
Similarity-based reasoning
Strong entailment
Approximate entailment
Logical calculi
Non-classical logic
Similarity-based reasoning
Strong entailment
Artificial intelligence
Fuzzy sets
Biomineralization
spellingShingle Approximate entailment
Non-classical logics
Similarity-based reasoning
Strong entailment
Approximate entailment
Logical calculi
Non-classical logic
Similarity-based reasoning
Strong entailment
Artificial intelligence
Fuzzy sets
Biomineralization
Logics for approximate and strong entailments
topic_facet Approximate entailment
Non-classical logics
Similarity-based reasoning
Strong entailment
Approximate entailment
Logical calculi
Non-classical logic
Similarity-based reasoning
Strong entailment
Artificial intelligence
Fuzzy sets
Biomineralization
description We consider two kinds of similarity-based reasoning and formalise them in a logical setting. In one case, we are led by the principle that conclusions can be drawn even if they are only approximately correct. This leads to a graded approximate entailment, which is weaker than classical entailment. In the other case, we follow the principle that conclusions must remain correct even if the assumptions are slightly changed. This leads to a notion of a graded strong entailment, which is stronger than classical entailment. We develop two logical calculi based on the notions of approximate and of strong entailment, respectively. © 2011 Elsevier B.V.
title Logics for approximate and strong entailments
title_short Logics for approximate and strong entailments
title_full Logics for approximate and strong entailments
title_fullStr Logics for approximate and strong entailments
title_full_unstemmed Logics for approximate and strong entailments
title_sort logics for approximate and strong entailments
publishDate 2012
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01650114_v197_n_p59_Esteva
http://hdl.handle.net/20.500.12110/paper_01650114_v197_n_p59_Esteva
_version_ 1768544174880063488

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