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 44
Page 165
... CLINT and MIS . Whereas most recent work in Inductive Logic Programming focuses on Empirical Inductive Logic Programming , our approach is situated in Interac- tive Inductive Logic Programming ... CLINT 165 The logical framework of CLINT 3.
... CLINT and MIS . Whereas most recent work in Inductive Logic Programming focuses on Empirical Inductive Logic Programming , our approach is situated in Interac- tive Inductive Logic Programming ... CLINT 165 The logical framework of CLINT 3.
Page 413
... CLINT however first constructs a minimal generalization of an hypothesis clause consistent with N , then generates ... CLINT and with the examples generated by CLINT , and if both systems were to use the same hypothesis language , the ...
... CLINT however first constructs a minimal generalization of an hypothesis clause consistent with N , then generates ... CLINT and with the examples generated by CLINT , and if both systems were to use the same hypothesis language , the ...
Page 414
... CLINT and GOLEM have such a restriction ; IRES does not . The introduction of a series of languages gives the advantage of being able to shift the bias automatically . The languages of GOLEM or CLINT could also be useful for IRES . The ...
... CLINT and GOLEM have such a restriction ; IRES does not . The introduction of a series of languages gives the advantage of being able to shift the bias automatically . The languages of GOLEM or CLINT could also be useful for IRES . The ...
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