Name
Abstract | ||
|---|---|---|
A number of generalizations of answer set programming have been proposed in the literature to deal with vagueness, uncertainty, and partial rule satisfaction. We introduce a unifying framework that entails most of the existing approaches to fuzzy answer set programming. In this framework, rule bodies are defined using arbitrary fuzzy connectives with monotone partial mappings. As an approximation of full answer sets, k ---answer sets are introduced to deal with conflicting information, yielding a flexible framework that encompasses, among others, existing work on valued constraint satisfaction and answer set optimization. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-02282-1_44 | WILF |
Keywords | Field | DocType |
arbitrary fuzzy connective,full answer set,answer set,constraint satisfaction,existing approach,general fuzzy answer set,existing work,unifying framework,fuzzy answer set programming,answer set programming,flexible framework | Data mining,Computer science,Theoretical computer science,Stable model semantics,Artificial intelligence,Answer set programming,Monotone polygon,Constraint satisfaction,Vagueness,Generalization,Fuzzy logic,Type-2 fuzzy sets and systems,Machine learning | Conference |
Volume | ISSN | Citations |
5571 | 0302-9743 | 15 |
PageRank | References | Authors |
0.58 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeroen Janssen | 1 | 71 | 6.17 |
| Steven Schockaert | 2 | 583 | 57.95 |
| Dirk Vermeir | 3 | 694 | 85.34 |
| Martine De Cock | 4 | 1341 | 96.06 |