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 215
... method of carrying out inductive learning within first - order logic . We have called this new method " inverse res- olution " . A Prolog - coded system called CIGOL which embodies a prototype of some of the ideas behind inverse ...
... method of carrying out inductive learning within first - order logic . We have called this new method " inverse res- olution " . A Prolog - coded system called CIGOL which embodies a prototype of some of the ideas behind inverse ...
Page 223
... method for updating rule - based systems and ( 2 ) to apply this method to the problem of integrity constraints . We will deal with these in turn though it should be remembered that the theoretical method and the practical application ...
... method for updating rule - based systems and ( 2 ) to apply this method to the problem of integrity constraints . We will deal with these in turn though it should be remembered that the theoretical method and the practical application ...
Page 300
... method , implemented in a system called SIERES , is based on a general - to- specific search guided by constraints on the form of clauses . Unlike FOIL , which searches in a very unconstrained space , SIERES it- eratively increases the ...
... method , implemented in a system called SIERES , is based on a general - to- specific search guided by constraints on the form of clauses . Unlike FOIL , which searches in a very unconstrained space , SIERES it- eratively increases the ...
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