Theory of generalized annotated logic programming and its applications
Journal of Logic Programming
A Transformation System for Developing Recursive Programs
Journal of the ACM (JACM)
Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence
Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence
Tabling for non-monotonic programming
Annals of Mathematics and Artificial Intelligence
Multi-adjoint Logic Programming with Continuous Semantics
LPNMR '01 Proceedings of the 6th International Conference on Logic Programming and Nonmonotonic Reasoning
Rules + strategies for transforming lazy functional logic programs
Theoretical Computer Science
Efficient Reductants Calculi using Partial Evaluation Techniques with Thresholding
Electronic Notes in Theoretical Computer Science (ENTCS)
Programming with Fuzzy Logic Rules by Using the FLOPER Tool
RuleML '08 Proceedings of the International Symposium on Rule Representation, Interchange and Reasoning on the Web
Prolog-ELF incorporating fuzzy logic
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 2
On fuzzy unfolding: A multi-adjoint approach
Fuzzy Sets and Systems
An unfolding-based preprocess for reinforcing thresholds in fuzzy tabulation
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Annotating "Fuzzy chance degrees" when debugging XPath queries
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
Hi-index | 0.00 |
Tabulation has been widely used in most (crisp) declarative paradigms for efficiently running programs without the redundant evaluation of goals. More recently, we have reinforced the original method in a fuzzy setting, by the dynamic generation of thresholds which avoid many useless computations leading to insignificant solutions. In this paper, we draw a static technique for generating such filters without requiring the consumption of extra computational resources at execution time.