Learning from good and bad data
Learning from good and bad data
Linear resolution for consequence finding
Artificial Intelligence
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Hypothesis finding based on upward refinement of residue hypotheses
Theoretical Computer Science - Selected papers in honour of Setsuo Arikawa
A completeness theorem and a computer program for finding theorems derivable from given axioms
A completeness theorem and a computer program for finding theorems derivable from given axioms
Induction as Consequence Finding
Machine Learning
Knowledge based discovery in systems biology using CF-induction
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Mode-directed inverse entailment for full clausal theories
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Comparison of upward and downward generalizations in CF-Induction
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
| Hi-index | 0.00 |
CF-induction is a sound and complete hypothesis finding procedure for full clausal logic which uses the principle of inverse entailment to compute a hypothesis that logically explains a set of examples with respect to a prior background theory. Currently, CF-induction computes hypotheses by applying combinations of several complex generalisation operators to an intermediate theory called a bridge formula. In this paper we propose an alternative approach whereby hypotheses are derived from a bridge formula using a single deductive operator and a single inductive operator. We show that our simplified procedure preserves the soundness and completeness of CF-induction.