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 52
... consistent with the set of examples , the LGG > , of these positive examples is consistent . Then check whether the LGG covers any negative examples . This check can be done in polynomial time using the constraint queries used by the ...
... consistent with the set of examples , the LGG > , of these positive examples is consistent . Then check whether the LGG covers any negative examples . This check can be done in polynomial time using the constraint queries used by the ...
Page 99
... consistent pair since they come from a model of a theory . The theory generated by the algorithm will ground enclose the true facts in this consistent pair with the language conjecture . It should also fail to imply any false facts ...
... consistent pair since they come from a model of a theory . The theory generated by the algorithm will ground enclose the true facts in this consistent pair with the language conjecture . It should also fail to imply any false facts ...
Page 217
... consistent , then infer the default . In non - monotonic logic , a modal logic was developed with a modal operator M , so that if p is a formula in the logic , then Mp has an intended meaning of " maybe p " or " p is consistent with ...
... consistent , then infer the default . In non - monotonic logic , a modal logic was developed with a modal operator M , so that if p is a formula in the logic , then Mp has an intended meaning of " maybe p " or " p is consistent with ...
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