Inductive Logic Programming: 13th International Conference, ILP 2003, Szeged, Hungary, September 29 - October 1, 2003, Proceedings, Volume 13

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Springer Science & Business Media, Sep 24, 2003 - Computers - 400 pages
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The13thInternationalConferenceonInductive 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 The program committee received altogether 58 submissions in response to the call for papers, of which 5 were withdrawn by the authors themselves.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
Author Index
401
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