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 234
... instances [ 1 , 50 ] . Although these algorithms differ in how they process instances and represent concept descriptions , they are generally applicable to a large set of supervised learning tasks . Current- generation variants of these ...
... instances [ 1 , 50 ] . Although these algorithms differ in how they process instances and represent concept descriptions , they are generally applicable to a large set of supervised learning tasks . Current- generation variants of these ...
Page 239
... instances until no satisfied negative instances remain . However , FOIL.1 employs an encoding restriction that prevents it from learning re- lations that require more bits to encode than the set of positive instances it generalizes ...
... instances until no satisfied negative instances remain . However , FOIL.1 employs an encoding restriction that prevents it from learning re- lations that require more bits to encode than the set of positive instances it generalizes ...
Page 243
... instances incrementally , generalizing them with reference to a set of background Horn clauses using a single generalization operator . It also conducts experiments by generating training instances to test its gener- alizations and ...
... instances incrementally , generalizing them with reference to a set of background Horn clauses using a single generalization operator . It also conducts experiments by generating training instances to test its gener- alizations and ...
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