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 45
... expression e1 corresponds to a term t2 in an expression e2 , then so does every other occurrence of t1 in e1 , provided e2 is an instance of e1 . In this case we sometimes speak of the term in e2 that corresponds to a given term ...
... expression e1 corresponds to a term t2 in an expression e2 , then so does every other occurrence of t1 in e1 , provided e2 is an instance of e1 . In this case we sometimes speak of the term in e2 that corresponds to a given term ...
Page 154
... expression DE as follows : either DE is a non - empty clause or DE is the expression ( ( DE1 , 1 ) · ( DE2 , 12 ) ) where DE1 and DE2 are derivation expressions and l1 and l2 are literals found in the clauses in DE1 and DE2 respectively ...
... expression DE as follows : either DE is a non - empty clause or DE is the expression ( ( DE1 , 1 ) · ( DE2 , 12 ) ) where DE1 and DE2 are derivation expressions and l1 and l2 are literals found in the clauses in DE1 and DE2 respectively ...
Page 351
... expression - building operators , a pruning criterion is derived from the acceptance criterion . This derived pruning criterion only prunes hypothe- ses which cannot fulfill the acceptance criterion , i.e. , if a hypothesis h fulfills ...
... expression - building operators , a pruning criterion is derived from the acceptance criterion . This derived pruning criterion only prunes hypothe- ses which cannot fulfill the acceptance criterion , i.e. , if a hypothesis h fulfills ...
Contents
Inductive Logic Programming | 4 |
A Framework for Inductive Logic Programming | 9 |
3 | 21 |
Copyright | |
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Common terms and phrases
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