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 48
... sentences in the presence of a background theory.5 Let C and D be classes of logical sentences , and let T be a class of logical theories . For any sentence 1 € C and 2 € D , and any background theory ET , 1 is said to cover 2 with ...
... sentences in the presence of a background theory.5 Let C and D be classes of logical sentences , and let T be a class of logical theories . For any sentence 1 € C and 2 € D , and any background theory ET , 1 is said to cover 2 with ...
Page 49
... sentences , with fixed Σ from a class T of theories , and with examples from a class D of logical sentences , if and only if for any fixed theory ΣeT there exists a polynomial p ( n , m ) such that : for any target sentence EC , when A ...
... sentences , with fixed Σ from a class T of theories , and with examples from a class D of logical sentences , if and only if for any fixed theory ΣeT there exists a polynomial p ( n , m ) such that : for any target sentence EC , when A ...
Page 98
... sentences need not be sentences in the model but also those that subsume sentences in the model . The extraneous predicates which are not essential for the theory need to be weeded out . Some of the definitions and lemmata that follow ...
... sentences need not be sentences in the model but also those that subsume sentences in the model . The extraneous predicates which are not essential for the theory need to be weeded out . Some of the definitions and lemmata that follow ...
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