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 384
... examples and no negative examples . There- fore , the fifth clause is no payment_due ( A ) : - disabled ( A ) . For the sixth clause , there are no remaining positive examples explained by eligible for deferment . However , continuously ...
... examples and no negative examples . There- fore , the fifth clause is no payment_due ( A ) : - disabled ( A ) . For the sixth clause , there are no remaining positive examples explained by eligible for deferment . However , continuously ...
Page 483
... examples that were randomly generated in simulation . In each case , the negative example set contained 540 facts and the background example set 17997. Induced rules were tested on 950 positive examples and 960 negative examples . The ...
... examples that were randomly generated in simulation . In each case , the negative example set contained 540 facts and the background example set 17997. Induced rules were tested on 950 positive examples and 960 negative examples . The ...
Page 499
... negative . LINUS induces range - restricted program clauses in which no new variables are introduced in the body of ... examples generation Negative examples of the target relation are not necessarily given ; they may be generated ...
... negative . LINUS induces range - restricted program clauses in which no new variables are introduced in the body of ... examples generation Negative examples of the target relation are not necessarily given ; they may be generated ...
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