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 216
Stephen Muggleton. 1.2 Non - monotonic logics Non - monotonicity is an important property of natural deductive reasoning . If a decision is drawn on the basis of a set of assertions , that decision may be withdrawn upon gaining a further ...
Stephen Muggleton. 1.2 Non - monotonic logics Non - monotonicity is an important property of natural deductive reasoning . If a decision is drawn on the basis of a set of assertions , that decision may be withdrawn upon gaining a further ...
Page 219
... non - monotonic logics in real - world problems . One of the arguments against using a non - monotonic reasoning strategy is the problem of coding of the non - monotonic knowledge - base adhoc so- lutions can seem more obvious to a ...
... non - monotonic logics in real - world problems . One of the arguments against using a non - monotonic reasoning strategy is the problem of coding of the non - monotonic knowledge - base adhoc so- lutions can seem more obvious to a ...
Page 224
... non- monotonic logic . Having a non - monotonic representation has advantages for reasoning with uncertain or missing data , and also has advantages for a rule discovery system . As highlighted in Figure 1 , if a set of discovered rules ...
... non- monotonic logic . Having a non - monotonic representation has advantages for reasoning with uncertain or missing data , and also has advantages for a rule discovery system . As highlighted in Figure 1 , if a set of discovered rules ...
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