First Order Inductive Learner

In machine learning, first-order inductive learner (FOIL) is a rule-based learning algorithm. In machine learning, first-order inductive learner (FOIL) is a rule-based learning algorithm. Developed in 1990 by Ross Quinlan, FOIL learns function-free Horn clauses, a subset of first-order predicate calculus. Given positive and negative examples of some concept and a set of background-knowledge predicates, FOIL inductively generates a logical concept definition or rule for the concept. The induced rule must not involve any constants (color(X,red) becomes color(X,Y), red(Y)) or function symbols, but may allow negated predicates; recursive concepts are also learnable. Like the ID3 algorithm, FOIL hill climbs using a metric based on information theory to construct a rule that covers the data. Unlike ID3, however, FOIL uses a separate-and-conquer method rather than divide-and-conquer, focusing on creating one rule at a time and collecting uncovered examples for the next iteration of the algorithm.