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 214
... structures and representations are either altered incrementally or , occasionally , completely overthrown . A practical learning algorithm must be capable of constructing and main- taining a growing structure of background knowledge ...
... structures and representations are either altered incrementally or , occasionally , completely overthrown . A practical learning algorithm must be capable of constructing and main- taining a growing structure of background knowledge ...
Page 345
... structure between rule mod- els to improve the search in the hypothesis space . Second , we define an order of the premises within a single rule model to make the pruning mechanism applicable to partially instantiated rule models ...
... structure between rule mod- els to improve the search in the hypothesis space . Second , we define an order of the premises within a single rule model to make the pruning mechanism applicable to partially instantiated rule models ...
Page 455
... structure and the loadings and boundary conditions . Usually it is necessary to make a few different meshes until ... structure into a collection of edges . Figure 3 shows some of the labellings of edges of the structure from Figure 1 ...
... structure and the loadings and boundary conditions . Usually it is necessary to make a few different meshes until ... structure into a collection of edges . Figure 3 shows some of the labellings of edges of the structure from Figure 1 ...
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