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 194
... explanation , which is not fixed in advance . As an additional benefit , this allows for alternative definitions of explanation even when the base logic is fixed . In particular , we introduce the notion of weak explanation as ...
... explanation , which is not fixed in advance . As an additional benefit , this allows for alternative definitions of explanation even when the base logic is fixed . In particular , we introduce the notion of weak explanation as ...
Page 198
... explanation ; and finding other possible notions of explanation . Additionally , it would be interesting to have a semantic account of the logic of explanations . An initial study can be found in [ 7 ] . 3 Induction of Strong Theories ...
... explanation ; and finding other possible notions of explanation . Additionally , it would be interesting to have a semantic account of the logic of explanations . An initial study can be found in [ 7 ] . 3 Induction of Strong Theories ...
Page 393
... explanation - based learning : a solution to the multiple explanation - problem . ML - TR - 29 , Rutgers University , New Brunswick , NJ , 1990 . [ 5 ] A. Danyluk . Finding new rules for incomplete theories : explicit biases for ...
... explanation - based learning : a solution to the multiple explanation - problem . ML - TR - 29 , Rutgers University , New Brunswick , NJ , 1990 . [ 5 ] A. Danyluk . Finding new rules for incomplete theories : explicit biases for ...
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