Title :
Towards inductive support logic programming
Author :
Baldwin, J.F. ; Martin, T.P.
Author_Institution :
Dept. of Eng. Math., Bristol Univ., UK
Abstract :
Support logic programming and its practical implementation (Fril) integrates probabilistic and fuzzy uncertainty into logic programming using mass assignments. This paper presents a snapshot of current research, aimed at combining the best aspects of inductive logic programming with the uncertainty representation of Fril to create a sophisticated and novel approach to knowledge discovery. An example is given showing how a supported Fril rule can be extracted from uncertain Fril relations
Keywords :
data mining; fuzzy logic; inductive logic programming; logic programming languages; probability; uncertainty handling; very large databases; Fril; data browser; data mining; fuzzy uncertainty; inductive support logic programming; knowledge discovery; large databases; mass assignments; probabilistic uncertainty; rule; Aging; Artificial intelligence; Data mining; Humans; Logic programming; Mathematics; Predictive models; Uncertainty; Visual databases; Visualization;
Conference_Titel :
IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-7078-3
DOI :
10.1109/NAFIPS.2001.944352
