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
... substitutions . Let E be a well - formed formula or a term and = { v1 / t1 , ... , vn / tn } be a substitution . The instantiation of E by 0 , written E0 , is formed by replacing every occurrence of v ; in E by t ;. Every sub - term ...
... substitutions . Let E be a well - formed formula or a term and = { v1 / t1 , ... , vn / tn } be a substitution . The instantiation of E by 0 , written E0 , is formed by replacing every occurrence of v ; in E by t ;. Every sub - term ...
Page 267
... substitution O is a most general unifier or MGU of t1 and t2 if and only if there is no other unifier for which the ... substitution . Given a term or literal t and a substitution 0 , there exists a unique inverse substitution 0-1 such ...
... substitution O is a most general unifier or MGU of t1 and t2 if and only if there is no other unifier for which the ... substitution . Given a term or literal t and a substitution 0 , there exists a unique inverse substitution 0-1 such ...
Page 347
... substitution applied to term variables as used in the definition of ≥ we use a substitution Σ applied to predicate variables to rename or instantiate the predicate variables . Note that a substitution σ applied to term variables always ...
... substitution applied to term variables as used in the definition of ≥ we use a substitution Σ applied to predicate variables to rename or instantiate the predicate variables . Note that a substitution σ applied to term variables always ...
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