## Inductive Logic Programming: 13th International Conference, ILP 2003, Szeged, Hungary, September 29 - October 1, 2003, Proceedings, Volume 13The13thInternationalConferenceonInductive LogicProgramming(ILP 2003), organizedbytheDepartmentofInformaticsattheUniversityofSzeged,washeld between September 29 and October 1, 2003 in Szeged, Hungary. ILP 2003 was co-located with the Kalm ́ ar Workshop on Logic and Computer Science devoted to the workofL ́ aszl ́oKalm ́ arandto recentresultsinlogicandcomputerscience. This volume contains all full papers presented at ILP 2003, together with the abstracts of the invited lectures by Ross D. King (University of Wales, Aber- twyth) and John W. Lloyd (Australian National University, Canberra). TheILP conferenceseries,startedin1991,wasoriginallydesignedto provide an international forum for the presentation and discussion of the latest research resultsinallareasoflearninglogicprograms.InrecentyearsthescopeofILPhas been broadened to cover theoretical, algorithmic, empirical, and applicational aspects of learning in non-propositional logic, multi-relational learning and data mining, and learning from structured and semi-structured data. The program committee received altogether 58 submissions in response to the call for papers, of which 5 were withdrawn by the authors themselves. Out of the remaining 53 submissions, the program committee selected 23 papers for full presentation at ILP 2003. High reviewing standards were applied for the selection of the papers. For the ?rst time, the "Machine Learning" journal awarded the best student papers. The awards were presented to Marta Arias for her theoretical paper withRoniKhardon:ComplexityParametersforFirst-OrderClasses,andtoKurt DriessensandThomasG ̈ artnerfortheirjointalgorithmicpaperwithJanRamon: Graph Kernels and Gaussian Processes for Relational Reinforcement Learning. |

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### Contents

A Personal View of How Best to Apply ILP | 1 |

Agents that Reason and Learn | 2 |

A Multirelational Approach | 4 |

Complexity Parameters for FirstOrder Classes | 22 |

A MultiRelational Decision Tree Learning Algorithm Implementation and Experiments | 38 |

Applying Theory Revision to the Design of Distributed Databases | 57 |

Disjunctive Learning with a SoftClustering Method | 75 |

ILP for Mathematical Discovery | 93 |

Ideal Reﬁnement of Descriptions in ALLog | 215 |

Which FirstOrder Logic Clauses Can Be Learned Using Genetic Algorithms? | 233 |

Improved Distances for Structured Data | 251 |

Induction of Enzyme Classes from Biological Databases | 269 |

Estimating Maximum Likelihood Parameters for Stochastic ContextFree Graph Grammars | 281 |

Induction of the Effects of Actions by Monotonic Methods | 299 |

A Generalisation of Progol | 311 |

Query Optimization in Inductive Logic Programming by Reordering Literals | 329 |

An Exhaustive Matching Procedure for the Improvement of Learning Efficiency | 112 |

Efficient Data Structures for Inductive Logic Programming | 130 |

Graph Kernels and Gaussian Processes for Relational Reinforcement Learning | 146 |

On Condensation of a Clause | 164 |

A Comparative Evaluation of Feature Set Evolution Strategies for Multirelational Boosting | 180 |

Comparative Evaluation of Approaches to Propositionalization | 197 |

Efficient Learning of Unlabeled Term Trees with Contractible Variables from Positive Data | 347 |

Relational IBL in Music with a New Structural Similarity Measure | 365 |

An Effective GrammarBased Compression Algorithm for Tree Structured Data | 383 |

401 | |

### Other editions - View all

Inductive Logic Programming: 13th International Conference, ILP ..., Volume 13 Tamas Horváth,Akihiro Yamamoto No preview available - 2003 |

### Common terms and phrases

AL-log algorithm applied approach arity Artificial Intelligence atoms average binary bottom clause Bottom Generalisation classification clique compression computed concept consider constants constraints contractible variable corresponding data mining data structure Datalog dataset defined definition denote distance distribution domain domain theory e(si edge efficiency elementary decomposition enzyme evaluation execution feature first-order first-order logic function Gaussian processes given graph grammars heuristic Horn Horn clause hyperedge hypothesis ILP systems implementation Inductive Logic Programming input instance instance-based learning item(X,Y label learner literals LNAI Machine Learning MRDTL Muggleton multi-relational multi-substitutions node non-determinacy parameters performance polynomial positive examples predicate problem procedure Progol5 Prolog query refinement operator regression reinforcement learning relational database representation represented RL-Trees selection graph set evolution similarity space Springer-Verlag subconcepts subset substitution subsumption target term subtree term tree Theorem theory tuples vertex vertices X:Order