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 6
... inductively constructed systems . It seems likely that technologies based on inductive inference will play an increasingly important role in future software development . 1.3 Shortcomings of present inductive systems Along with the ...
... inductively constructed systems . It seems likely that technologies based on inductive inference will play an increasingly important role in future software development . 1.3 Shortcomings of present inductive systems Along with the ...
Page 187
... Inductive Logic Programming . Certainly its interactive nature and the use of integrity constraints are important in this respect . We have also argued that there is a fundamental difference between In- teractive and Empirical Inductive ...
... Inductive Logic Programming . Certainly its interactive nature and the use of integrity constraints are important in this respect . We have also argued that there is a fundamental difference between In- teractive and Empirical Inductive ...
Page
Stephen Muggleton. THE APIC SERIES INDUCTIVE LOGIC PROGRAMMING Inductive logic programming is a new research area ... inductive logic programming • Coverage of induction of first order theories , the application of inductive logic ...
Stephen Muggleton. THE APIC SERIES INDUCTIVE LOGIC PROGRAMMING Inductive logic programming is a new research area ... inductive logic programming • Coverage of induction of first order theories , the application of inductive logic ...
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