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 202
... Figure 2 , we should be able to conclude that Sparky must be small ( assuming that the concept of sparrow is consistent ) . We can handle this line of reasoning deductively , if we add certain axioms to our background theory ( Figure 3 ) ...
... Figure 2 , we should be able to conclude that Sparky must be small ( assuming that the concept of sparrow is consistent ) . We can handle this line of reasoning deductively , if we add certain axioms to our background theory ( Figure 3 ) ...
Page 305
... Figure 2 . SIERES is provided with a set of argument dependency graphs ( Figure 3 ) , a kind of schemata . These argument dependency graphs are not detailed templates for the clauses , as for instance the rule models of Kietz and Wro ...
... Figure 2 . SIERES is provided with a set of argument dependency graphs ( Figure 3 ) , a kind of schemata . These argument dependency graphs are not detailed templates for the clauses , as for instance the rule models of Kietz and Wro ...
Page 403
... Figure 5 : An elaboration step Evaluation 3.3 The evaluation step uses the mapped ground literals and the refutation tree of the known example to construct a refutation tree for the target example . Starting from the ground literals the ...
... Figure 5 : An elaboration step Evaluation 3.3 The evaluation step uses the mapped ground literals and the refutation tree of the known example to construct a refutation tree for the target example . Starting from the ground literals the ...
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