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 135
... specific to more general using the pro- cedures Add , Generalize - I , Generalize - II , Generalize - D , Prove , Shrink and Create in his learning system . In Add , the logical trace of a new fact ( posi- tive example ) is added into ...
... specific to more general using the pro- cedures Add , Generalize - I , Generalize - II , Generalize - D , Prove , Shrink and Create in his learning system . In Add , the logical trace of a new fact ( posi- tive example ) is added into ...
Page 180
... specific , it does not know the difference between what is likely to be true ( allowed ) and what it does not know yet ( see Figure 1 ( b ) ) . If the system alternates between searching from general to specific and from specific to ...
... specific , it does not know the difference between what is likely to be true ( allowed ) and what it does not know yet ( see Figure 1 ( b ) ) . If the system alternates between searching from general to specific and from specific to ...
Page 310
... specific explanations for the training instances . SIERES assumes that the output argument of the body literal has to be the critical output term E in the general graph and its corresponding instan- tiations in the specific graphs ...
... specific explanations for the training instances . SIERES assumes that the output argument of the body literal has to be the critical output term E in the general graph and its corresponding instan- tiations in the specific graphs ...
Contents
Inductive Logic Programming | 4 |
A Framework for Inductive Logic Programming | 9 |
3 | 21 |
Copyright | |
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0-subsumes abstraction operators applied approach arguments arity Artificial Intelligence background knowledge Buntine C₁ C₂ CIGOL CLINT Closed World Assumption complete Computer concept description constrained atoms constraint predicates constraint theory constructed contains defined definite clauses 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 inductive learning Inductive Logic Programming inference input instance instantiation integrity constraints intended interpretation inverse resolution learnable learning algorithms Lemma LINUS literals Machine Learning method Morgan Kaufmann Muggleton multi-valued logic Negation as Failure negative examples non-monotonic logic occur oracle PAC-learning polynomial positive examples problem Prolog proof tree relation representation restricted RLGG rules saturation Section set of clauses Shapiro skolemized sort theory sorted atoms sparky specific subset substitution T₁ target Theorem true truncation tuples unit clauses variables