## Inductive Logic ProgrammingInductive logic programming is a new research area formed at the intersection of machine learning and logic programming. While the influence of logic programming has encouraged the development of strong theoretical foundations, this new area is inheriting its experimental orientation from machine learning. Inductive Logic Programming will be an invaluable text for all students of computer science, machine learning and logic programming at an advanced level. * * Examination of the background to current developments within the area * Identification of the various goals and aspirations for the increasing body of researchers in inductive logic programming * Coverage of induction of first order theories, the application of inductive logic programming and discussion of several logic learning programs * Discussion of the applications of inductive logic programming to qualitative modelling, planning and finite element mesh design |

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Page 338

on the form of hypotheses , or by using generalization / specialization operators

that produce only selected members of the

DeRaedt ' s CLINT ( 5 ) uses a series of languages that are defined by allowing ...

on the form of hypotheses , or by using generalization / specialization operators

that produce only selected members of the

**hypothesis space**. For example ,DeRaedt ' s CLINT ( 5 ) uses a series of languages that are defined by allowing ...

Page 345

The

predicates and the topology , is searched top - down ... The search is along the

generality hierarchy with pruning of specializations of accepted and failed

hypotheses .

The

**hypothesis space**, defined by the sets of available rule models andpredicates and the topology , is searched top - down ... The search is along the

generality hierarchy with pruning of specializations of accepted and failed

hypotheses .

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Given background knowledge A , a set of positive examples ε + and negative

examples E , CHAM finds a set of Horn clauses H such that : AAH Fet AAHHE 3 .

2

define ...

Given background knowledge A , a set of positive examples ε + and negative

examples E , CHAM finds a set of Horn clauses H such that : AAH Fet AAHHE 3 .

2

**Hypothesis space**CHAM employs the refinement operators in MIS ( 14 ) todefine ...

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### Contents

Inductive Logic Programming | 4 |

A Framework for Inductive Logic Programming | 9 |

oor A | 22 |

Copyright | |

26 other sections not shown

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### Common terms and phrases

algorithm allows applied approach arguments assume background knowledge base body called CIGOL CLINT complete Computer concept consistent constrained atoms constraint constructed contains correct corresponding covers defined definition derivation described domain theory efficient equivalent examples exists explanation expression extend facts false Figure finite first-order formula function given GOLEM ground head Horn clauses hypothesis implied inductive inference input instances Intelligence introduced inverse knowledge knowledge base language least limit literals Logic Programming Machine Learning method Muggleton negative examples non-monotonic Note occur operator ordinary polynomial positive positive examples possible predicates present problem Proceedings proof properties prove queries reasoning relation replacing representation representative resolution respect restricted result rules saturation sentences similar sorted atoms space specialization specific step structure substitution symbol Theorem theory tree true values variables