Probabilistic Semantics for Categorical Syllogisms of Figure II

Giuseppe Sanfilippo, Niki Pfeifer

Risultato della ricerca: Conference contribution

2 Citazioni (Scopus)

Abstract

A coherence-based probability semantics for categorical syllogisms of Figure I, which have transitive structures, has been proposed recently (Gilio, Pfeifer, & Sanfilippo [15]). We extend this work by studying Figure II under coherence. Camestres is an example of a Figure II syllogism: from Every P is M and No S is M infer No S is P. We interpret these sentences by suitable conditional probability assessments. Since the probabilistic inference of P¯|S from the premise set {M|P,M¯|S} is not informative, we add p(S|(S∨P))>0 as a probabilistic constraint (i.e., an “existential import assumption”) to obtain probabilistic informativeness. We show how to propagate the assigned (precise or interval-valued) probabilities to the sequence of conditional events (M|P,M¯|S,S|(S∨P)) to the conclusion P¯|S . Thereby, we give a probabilistic meaning to the other syllogisms of Figure II. Moreover, our semantics also allows for generalizing the traditional syllogisms to new ones involving generalized quantifiers (like Most S are P) and syllogisms in terms of defaults and negated defaults
Lingua originaleEnglish
Titolo della pubblicazione ospiteScalable Uncertainty Management. SUM 2018
Pagine196-211
Numero di pagine16
Stato di pubblicazionePublished - 2018

Serie di pubblicazioni

NomeLECTURE NOTES IN COMPUTER SCIENCE

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
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