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 24
... replacing every occurrence of v ; in E by t ;. Every sub - term within a given term or literal W can be uniquely referenced by its place within W. Places within terms or literals are denoted by n - tuples of natural numbers and defined ...
... replacing every occurrence of v ; in E by t ;. Every sub - term within a given term or literal W can be uniquely referenced by its place within W. Places within terms or literals are denoted by n - tuples of natural numbers and defined ...
Page 122
... replacing some or all occurrences of a constant in p by a variable not in p ; * A2 ( p ) is the set obtained by replacing some or all occurrences of a com- pound ground term in p by a variable not in p . Theorem 2 Aat is a set of ...
... replacing some or all occurrences of a constant in p by a variable not in p ; * A2 ( p ) is the set obtained by replacing some or all occurrences of a com- pound ground term in p by a variable not in p . Theorem 2 Aat is a set of ...
Page 267
Stephen Muggleton. = by replacing all occurrences of each v ; by the corresponding term t1 . The set of variables { 1 ... replacing all t ; at places { Pi , 1 , ... , Pi , m , } within t by v ;. Places within terms or literals are denoted ...
Stephen Muggleton. = by replacing all occurrences of each v ; by the corresponding term t1 . The set of variables { 1 ... replacing all t ; at places { Pi , 1 , ... , Pi , m , } within t by v ;. Places within terms or literals are denoted ...
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