## 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 |

### From inside the book

Results 1-3 of 77

Page 90

Proof of Theorem 4 Theorem PE A iff flat ( P ) , Flat Defs ( P ) E flat ( A ) , where

Flat Defs ( P ) denotes the set of flattening predicate definition clauses for P .

Proof : The theorem can be

Proof of Theorem 4 Theorem PE A iff flat ( P ) , Flat Defs ( P ) E flat ( A ) , where

Flat Defs ( P ) denotes the set of flattening predicate definition clauses for P .

Proof : The theorem can be

**proved**by induction on the complexity of terms of P ...Page 205

the incomplete example , if possible . The program uses the predicate resolve ,

which implements a resolution step for full clausal logic . extend ( PosNeg ,

NegPos , Extension ) :

F1 , F2 ) ...

the incomplete example , if possible . The program uses the predicate resolve ,

which implements a resolution step for full clausal logic . extend ( PosNeg ,

NegPos , Extension ) :

**prove**_ with axiom3 ( Pos Neg , F1 ) , provelist ( NegPos ,F1 , F2 ) ...

Page 276

At this point CIGOL tests to see whether E ' can be shown to be true or false using

T and N . This is based on depth - bounded theorem

Buntine ' s redundancy algorithm . When it cannot be shown to be true or false

the ...

At this point CIGOL tests to see whether E ' can be shown to be true or false using

T and N . This is based on depth - bounded theorem

**proving**, again usingBuntine ' s redundancy algorithm . When it cannot be shown to be true or false

the ...

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Contents

Inductive Logic Programming | 4 |

A Framework for Inductive Logic Programming | 9 |

oor A | 22 |

Copyright | |

26 other sections not shown

### Other editions - View all

### 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