Name
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.fss.2017.12.007 | Fuzzy Sets and Systems |
Keywords | Field | DocType |
Interval-based possibilistic logic,Conditioning,Possibility theory | Conditioning,Theoretical computer science,Possibility theory,Artificial intelligence,Possibilistic logic,Possibility distribution,Syntax,Machine learning,Mathematics | Journal |
Volume | ISSN | Citations |
343 | 0165-0114 | 1 |
PageRank | References | Authors |
0.36 | 17 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Salem Benferhat | 1 | 2585 | 216.23 |
| Vladik Kreinovich | 2 | 1091 | 281.07 |
| Amélie Levray | 3 | 8 | 3.56 |
| Karim Tabia | 4 | 107 | 25.11 |