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 98
... false in the model , then either the direct or indirect subtree must have a false root . The indirect subtree has a base clause in its root . If it is a ground clause , the oracle can tell whether the fact is true or false . If it is ...
... false in the model , then either the direct or indirect subtree must have a false root . The indirect subtree has a base clause in its root . If it is a ground clause , the oracle can tell whether the fact is true or false . If it is ...
Page 106
... false in the model which could be introduced into the theory by the algorithm at the point when all the facts in sets 1 to 3 above were available ; 5. representatives of all clauses false in the model which are introduced into the ...
... false in the model which could be introduced into the theory by the algorithm at the point when all the facts in sets 1 to 3 above were available ; 5. representatives of all clauses false in the model which are introduced into the ...
Page 182
... false and 2 ( KB , ē ) = false ; 2 ( KB , ē ) = true ; * YM ( KB , e ) = true iff * PM ( KB , e ) = false iff * PM ( KB , e ) = unknown iff 2 ( KB , e ) = false and 42 ( KB , ē ) = false ; = true and 2 ( KB , ē ) = true . * м ( KB , e ) ...
... false and 2 ( KB , ē ) = false ; 2 ( KB , ē ) = true ; * YM ( KB , e ) = true iff * PM ( KB , e ) = false iff * PM ( KB , e ) = unknown iff 2 ( KB , e ) = false and 42 ( KB , ē ) = false ; = true and 2 ( KB , ē ) = true . * м ( KB , e ) ...
Contents
Inductive Logic Programming | 4 |
A Framework for Inductive Logic Programming | 9 |
A Study of Constrained | 29 |
Copyright | |
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absorption abstraction operators applied approach arguments arity Artificial Intelligence background knowledge body Buntine C₁ C₂ CIGOL clause logic CLINT Closed World Assumption complete Computer concept descriptions constrained atoms constraint predicates constraint theory constructed contains defined derivation 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 incremental inductive learning Inductive Logic Programming inference input instances instantiation integrity constraints intended interpretation inverse resolution knowledge base learnable Lemma LINUS literals Machine Learning method Morgan Kaufmann Muggleton multi-valued logic negative examples non-monotonic logic oracle PAC-learnable polynomial positive examples problem Prolog proof tree queries recursive representation resolution step restricted result RLGG rules saturation Section set of clauses Shapiro skolemized sort theory sorted atoms sparky specific subset substitution target Theorem tion true truncation tuples unit clauses variables