## 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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... are of the corresponding types specified for input

z € Oj . To guide the induction process , LINUS uses meta - level knowledge ,

which can exclude any of the above four cases , thus reducing the search space .

... are of the corresponding types specified for input

**arguments**of function f ; andz € Oj . To guide the induction process , LINUS uses meta - level knowledge ,

which can exclude any of the above four cases , thus reducing the search space .

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3 Background knowledge In LINUS , background knowledge consists of a finite

set of utility ( background ) predicate and function definitions , together with the

types of their

typed ...

3 Background knowledge In LINUS , background knowledge consists of a finite

set of utility ( background ) predicate and function definitions , together with the

types of their

**arguments**. These predicate and function definitions are sets oftyped ...

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In what follows , 2 % of noise in

values of

value in five out of 100 examples , independently of noise in other

In what follows , 2 % of noise in

**argument**A means that in x % of examples , thevalues of

**argument**A were replaced by ... that its value was changed to a randomvalue in five out of 100 examples , independently of noise in other

**arguments**.### What people are saying - Write a review

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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