Various semantics for studying the square of opposition have been proposed recently. So far, only (Gilio et al., 2016) studied a probabilistic version of the square where the sentences were interpreted by (negated) defaults. We extend this work by interpreting sentences by imprecise (set-valued) probability assessments on a sequence of conditional events. We introduce the acceptability of a sentence within coherence-based probability theory. We analyze the relations of the square in terms of acceptability and show how to construct probabilistic versions of the square of opposition by forming suitable tripartitions. Finally, as an application, we present a new square involving generalized quantifiers.

Pfeifer, N., Sanfilippo, G. (2017). Square of Opposition Under Coherence. In Maria Brigida Ferraro, Paolo Giordani, Barbara Vantaggi, Marek Gagolewski, María Angeles Gil, Przemysław Grzegorzewski, et al. (a cura di), Soft Methods for Data Science (pp. 407-414). Springer International Publishing [10.1007/978-3-319-42972-4_50].

Square of Opposition Under Coherence

SANFILIPPO, Giuseppe
2017-01-01

Abstract

Various semantics for studying the square of opposition have been proposed recently. So far, only (Gilio et al., 2016) studied a probabilistic version of the square where the sentences were interpreted by (negated) defaults. We extend this work by interpreting sentences by imprecise (set-valued) probability assessments on a sequence of conditional events. We introduce the acceptability of a sentence within coherence-based probability theory. We analyze the relations of the square in terms of acceptability and show how to construct probabilistic versions of the square of opposition by forming suitable tripartitions. Finally, as an application, we present a new square involving generalized quantifiers.
2017
Pfeifer, N., Sanfilippo, G. (2017). Square of Opposition Under Coherence. In Maria Brigida Ferraro, Paolo Giordani, Barbara Vantaggi, Marek Gagolewski, María Angeles Gil, Przemysław Grzegorzewski, et al. (a cura di), Soft Methods for Data Science (pp. 407-414). Springer International Publishing [10.1007/978-3-319-42972-4_50].
File in questo prodotto:
File Dimensione Formato  
smps2016square.pdf

Solo gestori archvio

Descrizione: articolo
Dimensione 171.51 kB
Formato Adobe PDF
171.51 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/201424
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 12
  • ???jsp.display-item.citation.isi??? 10
social impact