1. APRIL, Application of Probabilistic Inductive Logic Programming, IST-2001-33053 Albert-Ludwigs-University, Freiburg, Germany & Imperial College of Science, Technology and Medicine, London, Great Britain. Albert-Ludwigs University Freiburg, Germany Applications of Probabilistic Inductive Logic Programming Imperial College of Science, Technology and Medicine, London, Great Britain

2. APRIL, Application of Probabilistic Inductive Logic Programming, IST-2001-33053 Albert-Ludwigs-University, Freiburg, Germany & Imperial College of Science, Technology and Medicine, London, Great Britain. Abstract The project adresses „probabilistic logic learning“ i.e. the integration of probabilistic reasoning with first order logic representations and machine learning. The objective is to critically assess the promise of this approach using a functional genomics application.

3. APRIL, Application of Probabilistic Inductive Logic Programming, IST-2001-33053 Albert-Ludwigs-University, Freiburg, Germany & Imperial College of Science, Technology and Medicine, London, Great Britain. Objectives 1.Are there significant applications for which "first order probabilistic logic" is better than state-of- the-art representations? 2.Can models be learned within such a "first order probabilistic logic"? Overall goal: To critically investigate "probabilistic logic learning" methods by answering the following questions:

4. APRIL, Application of Probabilistic Inductive Logic Programming, IST-2001-33053 Albert-Ludwigs-University, Freiburg, Germany & Imperial College of Science, Technology and Medicine, London, Great Britain. Description of Work In order to answer the questions, we plan 1.to investigate and evaluate various alternative first order probabilistic representations and reasoning mechanisms such as: oStochastic Logic Programs [Muggleton 95, Cussens 99], oBayesian Logic Programs" [Kersting, De Raedt 00]. 2.to employ the most promising such representations to model a functional genomics application; 3.to develop and to employ simple learning techniques to enable the learning of parts of the probabilistic logic representation; 4.to identify the main goals, directions and questions to be addressed in probabilistic logic learning.