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 47
... head of the LGG2 is the least ordinary generalization of the heads of the constrained atoms in E , and the constraint of the LGG ± 2 is the strongest possible constraint . Notice , however , that in this example the constraint contains ...
... head of the LGG2 is the least ordinary generalization of the heads of the constrained atoms in E , and the constraint of the LGG ± 2 is the strongest possible constraint . Notice , however , that in this example the constraint contains ...
Page 59
... head size greater than n but still require all others to have head size at most n . To see this , note that if B has head size greater than n , then it has head size greater than the size of the head ( B ' ) of the atom , ẞ ' , above it ...
... head size greater than n but still require all others to have head size at most n . To see this , note that if B has head size greater than n , then it has head size greater than the size of the head ( B ' ) of the atom , ẞ ' , above it ...
Page 282
... [ Head Tail ] , [ Head | Sublist1 ] , Sublist2 ) ← lte ( Head , X ) , partition ( X , Tail , Sublist1 , Sublist2 ) . ← ← partition ( X , [ Head Tail ] , Sublist1 , [ Head | Sublist2 ] ) ← gt ( Head , X ) , partition ( X , Tail ...
... [ Head Tail ] , [ Head | Sublist1 ] , Sublist2 ) ← lte ( Head , X ) , partition ( X , Tail , Sublist1 , Sublist2 ) . ← ← partition ( X , [ Head Tail ] , Sublist1 , [ Head | Sublist2 ] ) ← gt ( Head , X ) , partition ( X , Tail ...
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