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 82
... first time clearly stated as the basis for an inductive system . However , CIGOL suffered from many limitations . In order to give a hint of the complexity of CIGOL's method for solving inversion of resolution , we give , after [ 23 ] ...
... first time clearly stated as the basis for an inductive system . However , CIGOL suffered from many limitations . In order to give a hint of the complexity of CIGOL's method for solving inversion of resolution , we give , after [ 23 ] ...
Page 215
... first - order logic . This was based on the realization that the generalization operators used within Duce were all based on the idea of inverting Robinson's [ 16 ] resolution rule of deductive inference . This has led to the ...
... first - order logic . This was based on the realization that the generalization operators used within Duce were all based on the idea of inverting Robinson's [ 16 ] resolution rule of deductive inference . This has led to the ...
Page 300
... first- order logic programs . The approach of inverting resolution [ 3 , 10 , 13 ] is partic- ularly interesting because it offers a way to extend the vocabulary by inventing new predicates . However , the first implementations were too ...
... first- order logic programs . The approach of inverting resolution [ 3 , 10 , 13 ] is partic- ularly interesting because it offers a way to extend the vocabulary by inventing new predicates . However , the first implementations were too ...
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