Representing Knowledge about Norms

Daniel Kayser (1) , Farid Nouioua (1)

1 LIPN - Laboratoire d'Informatique de Paris-Nord

Norms are essential to extend inference: inferences based on norms are far richer than those based on logical implications. In the recent decades, much effort has been devoted to reason on a domain, once its norms are represented. How to extract and express those norms has received far less attention. Extraction is difficult: as the readers are supposed to know them, the norms of a domain are seldom made explicit. For one thing, extracting norms requires a language to represent them, and this is the topic of this paper. We apply this language to represent norms in the domain of driving, and show that it is adequate to reason on the causes of accidents, as described by car-crash reports.

Domaines: Intelligence artificielle [cs.AI]

Daniel Kayser, Farid Nouioua. Representing Knowledge about Norms. 2004, pp.363-367. ⟨hal-00085147⟩