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 39
... atoms . A set G is a CIG≥ of a set E of sorted atoms if and only if : * each member of G is a sorted atom that is E - more general than every member of E [ correctness ] ; * any sorted atom that is E - more general than every member of ...
... atoms . A set G is a CIG≥ of a set E of sorted atoms if and only if : * each member of G is a sorted atom that is E - more general than every member of E [ correctness ] ; * any sorted atom that is E - more general than every member of ...
Page 47
... atoms in E. Notice also that the constraint of the LGG , is the strongest possible constraint : strengthening the constraint yields a constrained atom that is not Σ - more general than both atoms in E. Example 16 Let E be { intimidates ...
... atoms in E. Notice also that the constraint of the LGG , is the strongest possible constraint : strengthening the constraint yields a constrained atom that is not Σ - more general than both atoms in E. Example 16 Let E be { intimidates ...
Page 60
... atoms is shattered by constrained atoms of head size at most n . We do so by showing that if such a set were shattered , there would exist a chain of constrained atoms , all but the bottom of which have head size at most n , of length ...
... atoms is shattered by constrained atoms of head size at most n . We do so by showing that if such a set were shattered , there would exist a chain of constrained atoms , all but the bottom of which have head size at most n , of length ...
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