Inductive Logic Programming

1. Front Matter

   PDF

2. Invited Papers

   1. Actions, Causation and Logic Programming

      • Vladimir Lifschitz

      Pages 1-1

   2. Challenges to Machine Learning: Relations Between Reality and Appearance

      • John McCarthy

      Pages 2-9

   3. First-Order Probabilistic Languages: Into the Unknown

      • Brian Milch, Stuart Russell

      Pages 10-24

   4. Integration of Learning and Reasoning Techniques

      • Bart Selman

      Pages 25-25

   5. Injecting Life with Computers

      • Ehud Shapiro

      Pages 26-26

3. Special Issue Extended Abstracts

   1. On the Connection Between the Phase Transition of the Covering Test and the Learning Success Rate

      • Erick Alphonse, Aomar Osmani

      Pages 27-29

   2. Revising Probabilistic Prolog Programs

      • Luc De Raedt, Kristian Kersting, Angelika Kimmig, Kate Revoredo, Hannu Toivonen

      Pages 30-33

   3. Inductive Logic Programming for Gene Regulation Prediction

      • Sebastian Fröhler, Stefan Kramer

      Pages 34-36

   4. QG/GA: A Stochastic Search for Progol

      • Stephen Muggleton, Alireza Tamaddoni-Nezhad

      Pages 37-39

   5. Generalized Ordering-Search for Learning Directed Probabilistic Logical Models

      • Jan Ramon, Tom Croonenborghs, Daan Fierens, Hendrik Blockeel, Maurice Bruynooghe

      Pages 40-42

   6. ALLPAD: Approximate Learning of Logic Programs with Annotated Disjunctions

      • Fabrizio Riguzzi

      Pages 43-45

   7. Margin-Based First-Order Rule Learning

      • Ulrich Rückert, Stefan Kramer

      Pages 46-48

4. Research Papers

   1. Extension of the Top-Down Data-Driven Strategy to ILP

      • Erick Alphonse, Céline Rouveirol

      Pages 49-63

   2. Extracting Requirements from Scenarios with ILP

      • Dalal Alrajeh, Oliver Ray, Alessandra Russo, Sebastian Uchitel

      Pages 64-78

   3. Learning Recursive Patterns for Biomedical Information Extraction

      • Margherita Berardi, Donato Malerba

      Pages 79-93

   4. Towards Learning Non-recursive LPADs by Transforming Them into Bayesian Networks

      • Hendrik Blockeel, Wannes Meert

      Pages 94-108

   5. Multi-class Prediction Using Stochastic Logic Programs

      • Jianzhong Chen, Lawrence Kelley, Stephen Muggleton, Michael Sternberg

      Pages 109-124

   6. Structuring Natural Language Data by Learning Rewriting Rules

      • Guillaume Cleuziou, Lionel Martin, Christel Vrain

      Pages 125-138

   7. An Efficient Algorithm for Computing Kernel Function Defined with Anti-unification

      • Koichiro Doi, Tetsuya Yamashita, Akihiro Yamamoto

      Pages 139-153

The inherent dangers of change are often summed up in the misquoted Chinese curse “May you live in interesting times.” The submission procedure for the 16th International Conference of Inductive Logic Programming (ILP 2006) was a radical (hopefully interesting but not cursed) departure from previous years. Submissions were requested in two phases. The ?rst phase involved submission of short papers (three pages) which were then presented at the conference and included in a short papers proceedings. In the second phase, reviewers selected papersforlongpapersubmission(15pagesmaximum).Thesewerethenassessed by the same reviewers, who then decided which papers to include in the journal special issue and proceedings. In the ?rst phase there were a record 77 papers, comparedto the usual20 orso long papersofpreviousyears.Eachpaper was- viewed by three reviewers. Out of these, 71 contributors were invited to submit long papers. Out of the long paper submissions, 7 were selected for the - chine Learning Journal special issue and 27 were accepted for the proceedings. In addition, two papers were nominated by Program Committee referees for the applications prize and two for the theory prize. The papers represent the div- sity and vitality in present ILP research including ILP theory, implementations, search and phase transition, distributed and large-scale learning, probabilistic ILP, biological applications, natural language learning and planning and action learning.

• Bayesian networks
• algorithm
• algorithmic learning
• algorithms
• bioinformatics
• classifier systems
• complexity
• deductive databases
• generalisation operators
• inductive inference
• inductive logic programming
• knowledge representation
• logic
• programming
• verification
• algorithm analysis and problem complexity

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