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
... equivalent with respect to D and if and only if they cover the same sentences of D with respect to Σ.6 A sentence 2 € D is labelled as a positive example by a sentence 1 C and a background theory ΣT if 1 covers 2 with respect to Σ ...
... equivalent with respect to D and if and only if they cover the same sentences of D with respect to Σ.6 A sentence 2 € D is labelled as a positive example by a sentence 1 C and a background theory ΣT if 1 covers 2 with respect to Σ ...
Page 49
... equivalent to an unknown target sentence of C with respect to a fixed background theory Σ . An oracle for equivalence queries answers yes to any given query if the predicted sentence is equivalent to the target with respect to Σ ; it ...
... equivalent to an unknown target sentence of C with respect to a fixed background theory Σ . An oracle for equivalence queries answers yes to any given query if the predicted sentence is equivalent to the target with respect to Σ ; it ...
Page 142
... equivalent . Of course , the original CIGOL is restrictive since the resolution is not deductively complete . But this can be extended easily using another inverse substitution , which is equivalent to truncation in IRES . 4 Conclusions ...
... equivalent . Of course , the original CIGOL is restrictive since the resolution is not deductively complete . But this can be extended easily using another inverse substitution , which is equivalent to truncation in IRES . 4 Conclusions ...
Contents
Inductive Logic Programming | 4 |
A Framework for Inductive Logic Programming | 9 |
A Study of Constrained | 29 |
Copyright | |
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absorption abstraction operators applied approach arguments arity Artificial Intelligence background knowledge body Buntine C₁ C₂ CIGOL clause logic CLINT Closed World Assumption complete Computer concept descriptions constrained atoms constraint predicates constraint theory constructed contains defined derivation 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 incremental inductive learning Inductive Logic Programming inference input instances instantiation integrity constraints intended interpretation inverse resolution knowledge base learnable Lemma LINUS literals Machine Learning method Morgan Kaufmann Muggleton multi-valued logic negative examples non-monotonic logic oracle PAC-learnable polynomial positive examples problem Prolog proof tree queries recursive representation resolution step restricted result RLGG rules saturation Section set of clauses Shapiro skolemized sort theory sorted atoms sparky specific subset substitution target Theorem tion true truncation tuples unit clauses variables