Probability Propagation in Selected Aristotelian Syllogisms

Giuseppe Sanfilippo, Niki Pfeifer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

This paper continues our work on a coherence-based probability semantics for Aristotelian syllogisms (Gilio, Pfeifer, and Sanfilippo, 2016; Pfeifer and Sanfilippo, 2018) by studying Figure III under coherence. We interpret the syllogistic sentence types by suitable conditional probability assessments. Since the probabilistic inference of $P|S$ from the premise set ${P|M, S|M}$ is not informative, we add $p(M|(S ee M))>0$ as a probabilistic constraint (i.e., an ``existential import assumption'') to obtain probabilistic informativeness. We show how to propagate the assigned premise probabilities to the conclusion. Thereby, we give a probabilistic meaning to all syllogisms of Figure~III. We discuss applications like generalised quantifiers (like Most $S$ are $P$) and (negated) defaults
Original languageEnglish
Title of host publicationSymbolic and Quantitative Approaches to Reasoning with Uncertainty 15th European Conference, ECSQARU 2019 Belgrade, Serbia, September 18–20, 2019 Proceedings
Pages419-431
Number of pages13
Publication statusPublished - 2019

Publication series

NameLECTURE NOTES IN ARTIFICIAL INTELLIGENCE

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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