## 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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### Contents

Inductive Logic Programming | 3 |

A Framework for Inductive Logic Programming | 9 |

A Study of Constrained | 29 |

Extensions of Inversion of Resolution Applied to Theory Com | 63 |

Learning Theoretical Terms | 93 |

193 | 112 |

Logic Program Synthesis from Good Examples | 113 |

A Critical Comparison of Various Methods Based on Inverse | 131 |

Efficient Learning of Logic Programs with NonDeterminate | 361 |

An InformationBased Approach to Integrating Empirical | 373 |

Analogical Reasoning for Logic Programming | 397 |

Some Thoughts on Inverse Resolution | 409 |

Department of Computer Science Katholieke Universiteit Leuven Celestij | 422 |

Experiments in Nonmonotonic FirstOrder Induction | 423 |

Learning Qualitative Models of Dynamic Systems | 437 |

The Application of Inductive Logic Programming to Finite | 453 |

NonMonotonic Learning | 145 |

An Overview of the Interactive ConceptLearner and Theory | 163 |

Using Confirmation The | 213 |

Relating Relational Learning Algorithms | 233 |

Wray Buntine | 261 |

Efficient Induction of Logic Programs | 281 |

Constraints for Predicate Invention | 299 |

Refinement Graphs for FOIL and LINUS | 319 |

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added addition algorithm allows applied approach arguments Artificial Intelligence assume background knowledge base body called CIGOL clause complete Computer concept consistent constrained atoms constraint constructed contains correct corresponding covers defined definition dependency derivation described domain domain theory efficient exists experiments expression extend facts fault Figure FOCL FOIL formula function gain given GOLEM ground head heuristic Horn clauses hypothesis inductive inference input instances International introduced knowledge language least limit LINUS literals Logic Programming Machine Learning mesh method Morgan Kaufmann Muggleton negative examples Note occur operator performed positive positive examples possible predicate present problem Proceedings proof prove reasoning relation replacing representation representative respect restricted rules shows similar sorted sorted atoms space specialization specific step structure substitution symbol Theorem theory tree true University values variables