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 23
... called a positive literal and A is called a negative literal . The literals A and A are said to be each other's complements and form , in either order , a complementary pair . A finite set ( possibly empty ) of literals is called a ...
... called a positive literal and A is called a negative literal . The literals A and A are said to be each other's complements and form , in either order , a complementary pair . A finite set ( possibly empty ) of literals is called a ...
Page 65
... called i - completeness , and we will prove that combina- tion of saturation and truncation achieves a restricted form of i - completeness . We finally address the control issue for these operators ( Section 6 ) . More precisely , we ...
... called i - completeness , and we will prove that combina- tion of saturation and truncation achieves a restricted form of i - completeness . We finally address the control issue for these operators ( Section 6 ) . More precisely , we ...
Page 94
... called " theoretical terms " . Clauses having theo- retical terms as consequents will be called theoretical clauses . Base clauses whose consequents do not contain theoretical terms are called ground clauses ( this causes no conflict ...
... called " theoretical terms " . Clauses having theo- retical terms as consequents will be called theoretical clauses . Base clauses whose consequents do not contain theoretical terms are called ground clauses ( this causes no conflict ...
Contents
Inductive Logic Programming | 4 |
A Framework for Inductive Logic Programming | 9 |
A Study of Constrained | 29 |
Copyright | |
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absorption abstraction operators applied approach arguments arity Artificial Intelligence background knowledge body Buntine C₁ C₂ CIGOL clause logic CLINT Closed World Assumption complete Computer concept descriptions constrained atoms constraint predicates constraint theory constructed contains defined derivation 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 incremental inductive learning Inductive Logic Programming inference input instances instantiation integrity constraints intended interpretation inverse resolution knowledge base learnable Lemma LINUS literals Machine Learning method Morgan Kaufmann Muggleton multi-valued logic negative examples non-monotonic logic oracle PAC-learnable polynomial positive examples problem Prolog proof tree queries recursive representation resolution step restricted result RLGG rules saturation Section set of clauses Shapiro skolemized sort theory sorted atoms sparky specific subset substitution target Theorem tion true truncation tuples unit clauses variables