About Norms and Causes

Knowing the norms of a domain is crucial, but there exist no repository of norms. We propose a method to extract them from texts: texts generally do not describe a norm, but rather how a state-of-affairs differs from it. Answers about the cause of the state-of-affairs described often reveal the implicit norm. We apply this idea to the domain of driving, and validate it by designing algorithms that identify, in a text, the “basic” norms to which it refers implicitly.

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Additional Info

Field Value
Source Proc. 17th International Florida Artificial Intelligence Research Society Conference (FLAIRS)
Author Kayser, Daniel, Nouioua, Farid
Maintainer CCSD
Last Updated May 7, 2026, 21:20 (UTC)
Created May 7, 2026, 21:20 (UTC)
Identifier hal-00091630
Language en
contributor Laboratoire d'Informatique de Paris-Nord (LIPN) ; Université Paris 13 (UP13)-Institut Galilée-Université Sorbonne Paris Cité (USPC)-Centre National de la Recherche Scientifique (CNRS)
creator Kayser, Daniel
date 2004-05-07T00:00:00
harvest_object_id f2a9c5bd-65de-43d1-8a6e-13d95eb6e3d9
harvest_source_id 3374d638-d20b-4672-ba96-a23232d55657
harvest_source_title test moissonnage SELUNE
metadata_modified 2024-11-29T00:00:00
set_spec type:COMM