Inductive Logic ProgrammingStephen Muggleton Inductive logic programming is a new research area emerging at present. Whilst inheriting various positive characteristics of the parent subjects of logic programming an machine learning, it is hoped that the new area will overcome many of the limitations of its forbears. This book describes the theory, implementations and applications of Inductive Logic Programming. |
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Page 83
... perform given one example clause and a domain theory , and then other choices for the truncation operator . With sat- uration , no choice is performed until the application of truncation . Instead of choosing which absorption and then ...
... perform given one example clause and a domain theory , and then other choices for the truncation operator . With sat- uration , no choice is performed until the application of truncation . Instead of choosing which absorption and then ...
Page 508
... performed . In the first series , five sets of clauses were induced , one for each of the small training sets ( 100 ... performed slightly worse than FOIL , this result is not significant ( even at the 20 % level ) . On the large ...
... performed . In the first series , five sets of clauses were induced , one for each of the small training sets ( 100 ... performed slightly worse than FOIL , this result is not significant ( even at the 20 % level ) . On the large ...
Page 513
... performed on non - noisy training examples . Next , several series of experi- ments were conducted where various amounts of noise were introduced into the training examples . Both LINUS and FOIL performed well , even in the presence of ...
... performed on non - noisy training examples . Next , several series of experi- ments were conducted where various amounts of noise were introduced into the training examples . Both LINUS and FOIL performed well , even in the presence of ...
Contents
Inductive Logic Programming | 4 |
A Framework for Inductive Logic Programming | 9 |
A Study of Constrained | 29 |
Copyright | |
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Common terms and phrases
absorption abstraction operators applied approach arguments arity Artificial Intelligence background knowledge body Buntine C₁ C₂ CIGOL clause logic CLINT Closed World Assumption complete Computer concept descriptions constrained atoms constraint predicates constraint theory constructed contains defined derivation described domain theory e₁ efficient facts Figure finite first-order first-order logic flattening FOCL FOIL formula framework function symbols given GOLEM ground clause head heuristic Horn clauses hypothesis implied incremental inductive learning Inductive Logic Programming inference input instances instantiation integrity constraints intended interpretation inverse resolution knowledge base learnable Lemma LINUS literals Machine Learning method Morgan Kaufmann Muggleton multi-valued logic negative examples non-monotonic logic oracle PAC-learnable polynomial positive examples problem Prolog proof tree queries recursive representation resolution step restricted result RLGG rules saturation Section set of clauses Shapiro skolemized sort theory sorted atoms sparky specific subset substitution target Theorem tion true truncation tuples unit clauses variables