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
... background knowledge . Human inductive rea- soners make use of vast amounts of background knowledge when learn- ing . Inductive algorithms such as ID3 use only a fixed set of attributes attached to each example . Explanation - based ...
... background knowledge . Human inductive rea- soners make use of vast amounts of background knowledge when learn- ing . Inductive algorithms such as ID3 use only a fixed set of attributes attached to each example . Explanation - based ...
Page 83
... background knowledge . The algorithm first builds an h - easy ground finite model of the background knowledge . PGA then builds the generalization of all the pairs of these facts , provided that they are relevant to the concept to be ...
... background knowledge . The algorithm first builds an h - easy ground finite model of the background knowledge . PGA then builds the generalization of all the pairs of these facts , provided that they are relevant to the concept to be ...
Page 500
... Background knowledge In LINUS , background knowledge consists of a finite set of utility ( back- ground ) predicate and function definitions , together with the types of their arguments . These predicate and function definitions are ...
... Background knowledge In LINUS , background knowledge consists of a finite set of utility ( back- ground ) predicate and function definitions , together with the types of their arguments . These predicate and function definitions are ...
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