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 190
... Morgan Kaufmann , 1983 . [ 18 ] T.M. Mitchell . Version spaces : a candidate elimination approach to rule learning . In Proceedings of the 5th International Joint Conference on Artificial Intelligence , Morgan Kaufmann , 1977 . [ 19 ] ...
... Morgan Kaufmann , 1983 . [ 18 ] T.M. Mitchell . Version spaces : a candidate elimination approach to rule learning . In Proceedings of the 5th International Joint Conference on Artificial Intelligence , Morgan Kaufmann , 1977 . [ 19 ] ...
Page 258
... Morgan Kaufmann , 1986 . [ 52 ] J.C. Schlimmer . Incremental adjustment of representations for learning . In Proceedings of the Fourth International Workshop on Machine Learning ( pp . 79ā90 ) . Irvine , CA : Morgan Kaufmann , 1987 ...
... Morgan Kaufmann , 1986 . [ 52 ] J.C. Schlimmer . Incremental adjustment of representations for learning . In Proceedings of the Fourth International Workshop on Machine Learning ( pp . 79ā90 ) . Irvine , CA : Morgan Kaufmann , 1987 ...
Page 393
... Morgan Kaufmann , Ithaca , NY , 1989 . [ 2 ] F. Bergadano and A. Giordana . A knowledge intensive approach to concept induction . In Proceedings of the Fifth International Conference on Machine Learning , pages 305- 317 , Morgan Kaufmann ...
... Morgan Kaufmann , Ithaca , NY , 1989 . [ 2 ] F. Bergadano and A. Giordana . A knowledge intensive approach to concept induction . In Proceedings of the Fifth International Conference on Machine Learning , pages 305- 317 , Morgan Kaufmann ...
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 Cā Cā CIGOL clause logic CLINT Closed World Assumption complete Computer concept descriptions constrained atoms constraint predicates constraint theory constructed contains defined derivation described domain theory eā 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