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. |
From inside the book
Results 1-3 of 50
Page 34
... constrained atoms are defined as follows . Let / C be a constrained atom.1 Then ( o / C ) is equivalent to V ( C → ) and ( C ) is equivalent to ( C ^ ☀ ) . It is possible for a conjunction of atomic formulas built from constraint ...
... constrained atoms are defined as follows . Let / C be a constrained atom.1 Then ( o / C ) is equivalent to V ( C → ) and ( C ) is equivalent to ( C ^ ☀ ) . It is possible for a conjunction of atomic formulas built from constraint ...
Page 47
... atoms in E. Notice also that the constraint of the LGG , is the strongest possible constraint : strengthening the constraint yields a constrained atom that is not Σ - more general than both atoms in E. Example 16 Let E be { intimidates ...
... atoms in E. Notice also that the constraint of the LGG , is the strongest possible constraint : strengthening the constraint yields a constrained atom that is not Σ - more general than both atoms in E. Example 16 Let E be { intimidates ...
Page 52
... computes a con- strained atom consistent with a set of constrained atoms , where each atom is labelled as a positive or negative example , in time polynomial in the sizes of the examples can be used to PAC - learn constrained atoms . The ...
... computes a con- strained atom consistent with a set of constrained atoms , where each atom is labelled as a positive or negative example , in time polynomial in the sizes of the examples can be used to PAC - learn constrained atoms . The ...
Contents
Inductive Logic Programming | 4 |
A Framework for Inductive Logic Programming | 9 |
3 | 21 |
Copyright | |
44 other sections not shown
Other editions - View all
Common terms and phrases
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