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 43
... algorithm has E , and so can find which sorts appear in Σ , then oracles for two weaker kinds of queries are sufficient . In a query of the first kind , the algorithm asks whether the sort theory entails Vr ( a ) for a given sort 7 and ...
... algorithm has E , and so can find which sorts appear in Σ , then oracles for two weaker kinds of queries are sufficient . In a query of the first kind , the algorithm asks whether the sort theory entails Vr ( a ) for a given sort 7 and ...
Page 94
... algorithm . Some of the procedures arise directly out of my previous work with Sammut [ 16 ] . The induction algorithm does not start with any initial knowledge of the language . Unlike in Shapiro's system , the algorithm tries to infer ...
... algorithm . Some of the procedures arise directly out of my previous work with Sammut [ 16 ] . The induction algorithm does not start with any initial knowledge of the language . Unlike in Shapiro's system , the algorithm tries to infer ...
Page 390
... algorithm ( purely empirical versus combined empirical and explanation ) × 10 training set size ( 100 , 200 , ... , 1000 ) design . The dependent variable measured was the accuracy of the learning algorithm . Figure 3 shows the accuracy ...
... algorithm ( purely empirical versus combined empirical and explanation ) × 10 training set size ( 100 , 200 , ... , 1000 ) design . The dependent variable measured was the accuracy of the learning algorithm . Figure 3 shows the accuracy ...
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