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 148
... approach leads to new problems in defining the notions of generality and redundancy within this formalism . 1.4 Non - monotonic formalisms One of the most common approaches to non - monotonic reasoning is based on the Closed World ...
... approach leads to new problems in defining the notions of generality and redundancy within this formalism . 1.4 Non - monotonic formalisms One of the most common approaches to non - monotonic reasoning is based on the Closed World ...
Page 190
... Approach , Vol . 1 , Morgan Kaufmann , 1983 . [ 18 ] T.M. Mitchell . Version spaces : a candidate elimination approach to rule learning . In Proceedings of the 5th International Joint Conference on Artificial Intelligence , Morgan ...
... Approach , Vol . 1 , Morgan Kaufmann , 1983 . [ 18 ] T.M. Mitchell . Version spaces : a candidate elimination approach to rule learning . In Proceedings of the 5th International Joint Conference on Artificial Intelligence , Morgan ...
Page 221
... approach which combines Bayesian and Popperian ideas . Gillies attempted to develop such a combined approach in his 1988 paper [ 8 ] . The idea is that Popper's criticisms of Bayesianism do not prove it to be entirely wrong , but place ...
... approach which combines Bayesian and Popperian ideas . Gillies attempted to develop such a combined approach in his 1988 paper [ 8 ] . The idea is that Popper's criticisms of Bayesianism do not prove it to be entirely wrong , but place ...
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