Dagstuhl Seminar Proceedings, Volume 5051,

• Luc De Raedt, Tom Dietterich, Lise Getoor, and Stephen H. Muggleton 05051 Abstracts Collection – Probabilistic, Logical and Relational Learning - Towards a Synthesis 10.4230/DagSemProc.05051.1
• Luc De Raedt, Tom Dietterich, Lise Getoor, and Stephen H. Muggleton 05051 Executive Summary – Probabilistic, Logical and Relational Learning - Towards a Synthesis 10.4230/DagSemProc.05051.2
• John W. Lloyd and Tim D. Sears An Architecture for Rational Agents 10.4230/DagSemProc.05051.3
• Brian Milch, Bhaskara Marthi, Stuart Russell, David Sontag, Daniel L. Ong, and Andrey Kolobov BLOG: Probabilistic Models with Unknown Objects 10.4230/DagSemProc.05051.4
• Elias Gyftodimos and Peter A. Flach Combining Bayesian Networks with Higher-Order Data Representations 10.4230/DagSemProc.05051.5
• Nicos Angelopoulos and James Cussens Exploiting independence for branch operations in Bayesian learning of C&RTs 10.4230/DagSemProc.05051.6
• Manfred Jaeger Importance Sampling on Relational Bayesian Networks 10.4230/DagSemProc.05051.7
• Andrea Passerini, Paolo Frasconi, and Luc De Raedt Kernels on Prolog Proof Trees:Statistical Learning in the ILP Setting 10.4230/DagSemProc.05051.8
• Taisuke Sato and Yoshitaka Kameya Learning through failure 10.4230/DagSemProc.05051.9
• Jennifer Neville and David Jensen Leveraging relational autocorrelation with latent group models 10.4230/DagSemProc.05051.10
• Tobias Scheffer Multi-View Learning and Link Farm Discovery 10.4230/DagSemProc.05051.11