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 164
... knowledge base of predicate definitions . In this way , Interactive Inductive Logic Program- ming is more related to knowledge acquisition tools and learning apprentices such as DISCIPLE [ 36 ] and BLIP [ 20 , 39 ] and intensional ...
... knowledge base of predicate definitions . In this way , Interactive Inductive Logic Program- ming is more related to knowledge acquisition tools and learning apprentices such as DISCIPLE [ 36 ] and BLIP [ 20 , 39 ] and intensional ...
Page 174
... knowledge base KB is modified . Postponing an ex- ample occurs when the system does not have enough knowledge to derive a consistent clause covering the example . When an example is postponed , the knowledge base is temporarily left as ...
... knowledge base KB is modified . Postponing an ex- ample occurs when the system does not have enough knowledge to derive a consistent clause covering the example . When an example is postponed , the knowledge base is temporarily left as ...
Page 219
... knowledge - base adhoc so- lutions can seem more obvious to a knowledge engineer . However , an advan- tage of the non - monotonic approach is that it naturally fits into a dynamic knowledge - based system development . Some of the non ...
... knowledge - base adhoc so- lutions can seem more obvious to a knowledge engineer . However , an advan- tage of the non - monotonic approach is that it naturally fits into a dynamic knowledge - based system development . Some of the non ...
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