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 96
... tree in this restricted class of theories . Given S in T ( i ) the proof tree for S is defined as follows . If S E T then the proof tree of S is the single node S. This node is the main clause of the proof tree . If S is directly ...
... tree in this restricted class of theories . Given S in T ( i ) the proof tree for S is defined as follows . If S E T then the proof tree of S is the single node S. This node is the main clause of the proof tree . If S is directly ...
Page 97
... tree can be rewritten ( by the induction hypothesis ) in the desired form . We can then obtain a new proof of S from S ' by attaching the direct and indirect subtrees back as A ← B and the proof tree of C D. The only non- -base root of ...
... tree can be rewritten ( by the induction hypothesis ) in the desired form . We can then obtain a new proof of S from S ' by attaching the direct and indirect subtrees back as A ← B and the proof tree of C D. The only non- -base root of ...
Page 155
... trees . Figure 1 represents the transformation of a refutation tree into a derivation tree using “ deriv ” . In this figure both the refutation tree and the derivation tree represent general cases . The diagonal ellipses ( .. ) merely ...
... trees . Figure 1 represents the transformation of a refutation tree into a derivation tree using “ deriv ” . In this figure both the refutation tree and the derivation tree represent general cases . The diagonal ellipses ( .. ) merely ...
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