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
... reasoning . If a decision is drawn on the basis of a set of assertions , that decision may be withdrawn upon gaining ... reasoning mechanism must be resorted to . The development of formalisms to capture non - monotonic reasoning capa ...
... reasoning . If a decision is drawn on the basis of a set of assertions , that decision may be withdrawn upon gaining ... reasoning mechanism must be resorted to . The development of formalisms to capture non - monotonic reasoning capa ...
Page 217
... reasoning came from the consistency - based approaches of default logic [ 28 ] and non - monotonic logic I [ 17 ] . These formalize non - monotonic reasoning by the adoption of logical consistency : in other words , " in the absence of ...
... reasoning came from the consistency - based approaches of default logic [ 28 ] and non - monotonic logic I [ 17 ] . These formalize non - monotonic reasoning by the adoption of logical consistency : in other words , " in the absence of ...
Page 398
Stephen Muggleton. 1 Introduction Analogical reasoning is concerned with finding a solution for a new problem by making use of the known solution of a similar problem . Basically , analogical reasoning aims at mapping knowledge from a ...
Stephen Muggleton. 1 Introduction Analogical reasoning is concerned with finding a solution for a new problem by making use of the known solution of a similar problem . Basically , analogical reasoning aims at mapping knowledge from a ...
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