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 87
... means that there exists at least one model of T which is not a model for F ( c ) . In other words , there exists at ... means that in any model M of T , there is a constant assignation that associates c with an element d of the domain D ...
... means that there exists at least one model of T which is not a model for F ( c ) . In other words , there exists at ... means that in any model M of T , there is a constant assignation that associates c with an element d of the domain D ...
Page 171
... means implication in the knowledge base , whereas correctly covers means implication in the intended interpretation . The procedure abduction first initializes the list I with the goals corre- sponding to the instantiated bodies of ...
... means implication in the knowledge base , whereas correctly covers means implication in the intended interpretation . The procedure abduction first initializes the list I with the goals corre- sponding to the instantiated bodies of ...
Page 202
... means that the concept is a weak explanation for the example , because it does not necessarily assign the same classification to each instance of the example . The inclusion condition , on the other hand , means that the concept is a ...
... means that the concept is a weak explanation for the example , because it does not necessarily assign the same classification to each instance of the example . The inclusion condition , on the other hand , means that the concept is a ...
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