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 247
... GOLEM by Muggleton and Feng [ 39 ] They showed that the relative least general generalizations ( RLGGs ) of a set of ground facts relative to a set of background facts can be guaranteed to be finite in length ( i.e. , exponential in the ...
... GOLEM by Muggleton and Feng [ 39 ] They showed that the relative least general generalizations ( RLGGs ) of a set of ground facts relative to a set of background facts can be guaranteed to be finite in length ( i.e. , exponential in the ...
Page 413
... GOLEM computes the RLGG of the old hypothesis clause and a positive example , and if it is consistent wrt N , it replaces the old hypothesis clause by the new RLGG . CLINT however first constructs a minimal generalization of an ...
... GOLEM computes the RLGG of the old hypothesis clause and a positive example , and if it is consistent wrt N , it replaces the old hypothesis clause by the new RLGG . CLINT however first constructs a minimal generalization of an ...
Page 448
... GOLEM thus in a way found that the total amount of water in the system is constant . Note , however , that GOLEM only found a “ weak ” way of stating this . Instead of saying La + Lb = const . , Golem Tube only says that : d dt [ — La ] ...
... GOLEM thus in a way found that the total amount of water in the system is constant . Note , however , that GOLEM only found a “ weak ” way of stating this . Instead of saying La + Lb = const . , Golem Tube only says that : d dt [ — La ] ...
Contents
Inductive Logic Programming | 4 |
A Framework for Inductive Logic Programming | 9 |
3 | 21 |
Copyright | |
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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