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 81
... addition . Let us take any of the three functional relation constraints given for addition , for example : given plus ( X , Y , Z ) , if Y and Z are instantiated , there is only one possible instantiation for X. We also need the ...
... addition . Let us take any of the three functional relation constraints given for addition , for example : given plus ( X , Y , Z ) , if Y and Z are instantiated , there is only one possible instantiation for X. We also need the ...
Page 223
... addition of new information in the form of either data or rules . The prob- lem is to devise a method for modifying K appropriately in the light of new information . We will consider three cases separately . Feedback . If KA , it may be ...
... addition of new information in the form of either data or rules . The prob- lem is to devise a method for modifying K appropriately in the light of new information . We will consider three cases separately . Feedback . If KA , it may be ...
Page 390
... addition , we created one domain theory that was 70.2 % accurate by using the mutation operators of the previous Section . The design of this experiment follows a two - algorithm ( purely empirical versus combined empirical and ...
... addition , we created one domain theory that was 70.2 % accurate by using the mutation operators of the previous Section . The design of this experiment follows a two - algorithm ( purely empirical versus combined empirical and ...
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