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 34
... constraint predicates to be unsatisfiable with a constraint theory Σ . That is , it is possible to build a constraint C such that 3C . This would occur , for example , if a variable x were constrained to denote both a mammal and a ...
... constraint predicates to be unsatisfiable with a constraint theory Σ . That is , it is possible to build a constraint C such that 3C . This would occur , for example , if a variable x were constrained to denote both a mammal and a ...
Page 46
Stephen Muggleton. Constraint generalization algorithm Input : A constraint theory , E , and n constrained atoms , a ... predicates in Σ . For all 1 ≤r and each tuple ( each permutation of each set ) ( u1 , ... , u ) of l terms in Head : a .
Stephen Muggleton. Constraint generalization algorithm Input : A constraint theory , E , and n constrained atoms , a ... predicates in Σ . For all 1 ≤r and each tuple ( each permutation of each set ) ( u1 , ... , u ) of l terms in Head : a .
Page 58
... constraints is tighter than the second . Note that for a constraint theory , E , containing C constraint predicates , the maximum chain of sets of constraint predicates ordered by < has length at most C. This is because any set in such ...
... constraints is tighter than the second . Note that for a constraint theory , E , containing C constraint predicates , the maximum chain of sets of constraint predicates ordered by < has length at most C. This is because any set in such ...
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