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 43
... queries to this oracle taxonomic queries . Without the oracle , the answers to taxonomic queries cannot always be computed , but restrictions may be placed on the sort theory so that they can be . Alternatively , if the algorithm has E ...
... queries to this oracle taxonomic queries . Without the oracle , the answers to taxonomic queries cannot always be computed , but restrictions may be placed on the sort theory so that they can be . Alternatively , if the algorithm has E ...
Page 50
... queries is bounded by some polyno- mial in the size of the query . In the second problem , C and D are both the class of universally closed constrained atoms , and T is the class of constraint theories for which the time to answer ...
... queries is bounded by some polyno- mial in the size of the query . In the second problem , C and D are both the class of universally closed constrained atoms , and T is the class of constraint theories for which the time to answer ...
Page 52
... queries implies poly- nomial PAC - learning . More specifically , any algorithm that computes a con- strained atom consistent with a set of constrained atoms , where each atom is labelled as a positive or negative example , in time ...
... queries implies poly- nomial PAC - learning . More specifically , any algorithm that computes a con- strained atom consistent with a set of constrained atoms , where each atom is labelled as a positive or negative example , in time ...
Contents
Inductive Logic Programming | 4 |
A Framework for Inductive Logic Programming | 9 |
3 | 21 |
Copyright | |
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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