Qualitative conditioning in an interval-based possibilistic setting
Publication Date
5-2018
Abstract
Possibility theory and possibilistic logic are well-known uncertainty frameworks particularly suited for representing and reasoning with uncertain, partial and qualitative information. Belief update plays a crucial role when updating beliefs and uncertain pieces of information in the light of new evidence. This paper deals with conditioning uncertain information in a qualitative interval-valued possibilistic setting. The first important contribution concerns a set of three natural postulates for conditioning interval-based possibility distributions. We show that any interval-based conditioning satisfying these three postulates is necessarily based on the set of compatible standard possibility distributions. The second contribution consists in a proposal of efficient procedures to compute the lower and upper endpoints of the conditional interval-based possibility distribution while the third important contribution provides a syntactic counterpart of conditioning interval-based possibility distributions in case where these latter are compactly encoded in the form of possibilistic knowledge bases.
Comments
Technical Report: UTEP-CS-18-41
Published in Fuzzy Sets and Systems, 2018, Vol. 343, No. 1, pp. 35-49.
S. Benferhat et al, "Qualitative conditioning in an interval-based possibilistic setting," Fuzzy Sets and Systems, vol. 343, pp. 35-49, 2018. . DOI: https://doi.org/10.1016/j.fss.2017.12.007.