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. |
From inside the book
Results 1-3 of 72
Page 60
... arguments are the same fails to cover the third atom . Because these are orthogonal , they can be combined to ... arguments are the same . The atom p ( b , b , x , x ) excludes the first atom because the first argument is a b , the ...
... arguments are the same fails to cover the third atom . Because these are orthogonal , they can be combined to ... arguments are the same . The atom p ( b , b , x , x ) excludes the first atom because the first argument is a b , the ...
Page 326
... arguments of function f ; and z Є Oj . To guide the induction process , LINUS uses meta - level knowledge , which ... arguments of t of type Ti ; from the arguments of type T , ( Arg ) different literals of the form X11 = X12 can be ...
... arguments of function f ; and z Є Oj . To guide the induction process , LINUS uses meta - level knowledge , which ... arguments of t of type Ti ; from the arguments of type T , ( Arg ) different literals of the form X11 = X12 can be ...
Page 509
... arguments . The class variable was treated as an additional argument [ 6 ] when introducing noise . The percentage ... arguments are combined when applying background predicates to them . For instance , consider the predicate ( WRf = BKƒ ) ...
... arguments . The class variable was treated as an additional argument [ 6 ] when introducing noise . The percentage ... arguments are combined when applying background predicates to them . For instance , consider the predicate ( WRf = BKƒ ) ...
Contents
Inductive Logic Programming | 4 |
A Framework for Inductive Logic Programming | 9 |
3 | 21 |
Copyright | |
44 other sections not shown
Other editions - View all
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