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. |
From inside the book
Results 1-3 of 91
Page 105
... introduced by Dream from the indicators of that theoretical term . This indicates that the clauses introduced by Dream will also be introduced by the Muggleton ' W ' . It will be noticed that some of the work of Add and Shrink could be ...
... introduced by Dream from the indicators of that theoretical term . This indicates that the clauses introduced by Dream will also be introduced by the Muggleton ' W ' . It will be noticed that some of the work of Add and Shrink could be ...
Page 328
... introduce new vari- ables , then T C Te , T≤ T and TT . However , if new variables are introduced , it can happen that T > T and / or T > T. If T ++ of the pos- itive tuples in Te are represented by one or more tuples in Te , the ...
... introduce new vari- ables , then T C Te , T≤ T and TT . However , if new variables are introduced , it can happen that T > T and / or T > T. If T ++ of the pos- itive tuples in Te are represented by one or more tuples in Te , the ...
Page 510
... introducing noise and the chosen noise level . The chosen amount of noise was first introduced into the training sets of 100 examples in the chosen way . Five sets of clauses were then induced , one for each of the five training sets of ...
... introducing noise and the chosen noise level . The chosen amount of noise was first introduced into the training sets of 100 examples in the chosen way . Five sets of clauses were then induced , one for each of the five training sets of ...
Contents
Inductive Logic Programming | 4 |
A Framework for Inductive Logic Programming | 9 |
3 | 21 |
Copyright | |
44 other sections not shown
Other editions - View all
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