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 257
... International Work- shop on Inductive Logic Programming ( pp . 291–307 ) . Vienna de Castelo , Portugal : Unpublished , 1991 . [ 34 ] I. Mozetic . Learning of qualitative models . In I. Bratko and N. Lavrač ( Eds . ) , Progress in ...
... International Work- shop on Inductive Logic Programming ( pp . 291–307 ) . Vienna de Castelo , Portugal : Unpublished , 1991 . [ 34 ] I. Mozetic . Learning of qualitative models . In I. Bratko and N. Lavrač ( Eds . ) , Progress in ...
Page 258
... International Workshop on Machine Learning ( pp . 79–90 ) . Irvine , CA : Morgan Kaufmann , 1987 . [ 53 ] R. Seshu . Solving the parity problem . In Proceedings of the Fourth European Working Session on Learning ( pp . 263–271 ) ...
... International Workshop on Machine Learning ( pp . 79–90 ) . Irvine , CA : Morgan Kaufmann , 1987 . [ 53 ] R. Seshu . Solving the parity problem . In Proceedings of the Fourth European Working Session on Learning ( pp . 263–271 ) ...
Page 393
... International Conference on Machine Learning , pages 305- 317 , Morgan Kaufmann , Ann Arbor , MI , 1988 . [ 3 ] C. Brunk and M. Pazzani . An investigation of noise tolerant relational learning algo- rithms . In Proceeding of the ...
... International Conference on Machine Learning , pages 305- 317 , Morgan Kaufmann , Ann Arbor , MI , 1988 . [ 3 ] C. Brunk and M. Pazzani . An investigation of noise tolerant relational learning algo- rithms . In Proceeding of the ...
Contents
Inductive Logic Programming | 4 |
A Framework for Inductive Logic Programming | 9 |
A Study of Constrained | 29 |
Copyright | |
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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