Handbook of logic in artificial intelligence and logic programming (vol. 3)
Compiling propositional weighted bases
Artificial Intelligence - Special issue on nonmonotonic reasoning
Propositional independence: formula-variable independence and forgetting
Journal of Artificial Intelligence Research
Defining relative likelihood in partially-ordered preferential structures
Journal of Artificial Intelligence Research
Extending uncertainty formalisms to linear constraints and other complex formalisms
International Journal of Approximate Reasoning
Compiling possibilistic knowledge bases
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Mastering the Processing of Preferences by Using Symbolic Priorities in Possibilistic Logic
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
On the compilation of stratified belief bases under linear and possibilistic logic policies
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Database preferences queries: a possibilistic logic approach with symbolic priorities
FoIKS'08 Proceedings of the 5th international conference on Foundations of information and knowledge systems
Database preference queries--a possibilistic logic approach with symbolic priorities
Annals of Mathematics and Artificial Intelligence
Conditional preference nets and possibilistic logic
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
| Hi-index | 0.00 |
Possibilistic logic offers a convenient tool for handling uncertain or prioritized formulas and coping with inconsistency. Propositional logic formulas are thus associated with weights belonging to a linearly ordered scale. However, especially in case of multiple source information, only partial knowledge may be available about the relative ordering between weights of formulas. In order to cope with this problem, a two-sorted counterpart of possibilistic logic is introduced. Pieces of information are encoded as clauses where special literals refer to the weights. Constraints between weights translate into logical formulas of the corresponding sort and are gathered in a distinct auxiliary knowledge base. An inference relation, which is sound and complete with respect to preferential model semantics, enables us to draw plausible conclusions from the two knowledge bases. The inference process is characterized by using "forgetting variables" for handling the symbolic weights, and hence an inference process is obtained by means of a DNF compilation of the two knowledge bases.