Knowledge systems design
Inferring decision trees using the minimum description length principle
Information and Computation
Automating Knowledge Acquisition for Expert Systems
Automating Knowledge Acquisition for Expert Systems
Algorithmic Program DeBugging
Learning Logical Definitions from Relations
Machine Learning
Machine Learning
PAC-learnability of determinate logic programs
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Inductive logic programming and learnability
ACM SIGART Bulletin
Inductive Learning in Deductive Databases
IEEE Transactions on Knowledge and Data Engineering
Evolutionary program induction directed by logic grammars
Evolutionary Computation
Learning first order fuzzy logic rules
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
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A description is given of the FOIL (First-Order Inductive Learner) system, which exploits information from large numbers of examples to guide the search for a program. FOIL develops first-order rules from structured data described by a collection of relations. The guidance it provides turns out to be so effective that greedy search is usually adequate. Algorithms using greedy search, however, tend to suffer from a horizon effect: an action that might be desirable or even essential from a global perspective can appear relatively unpromising at a local level and so may be passed over. Rather than restricting the search space, and thus the class of learnable programs, FOIL exploits determinism to overcome some of the horizon effect of greedy search. The effect on learning time is usually negligible.