PLP 2015
Probabilistic Logic Programming

Table of Contents

• Preface 1-6
  Fabrizio Riguzzi, Joost Vennekens

Invited Talks

• BIMS: for Bayesian Inference of Model Structure 7
  Nicos Angelopoulos
• Probabilistic (Logic) Programming: Opportunities and Challenges 8
  Angelika Kimmig

Presentations

• Advances in Integrating Statistical Inference9-18
  Nicos Angelopoulos, Samer Abdallah, Georgios Giamas
  
• Towards a General Framework for Actual Causation Using CP-logic19-38
  Sander Beckers, Joost Vennekens
  
• Most Probable Explanation for MetaProbLog and Its Application in Heart Sound Segmentation39-45
  Theofrastos Mantadelis, Jorge Oliveira, Miguel Coimbra
  
• Constraint-Based Inference in Probabilistic Logic Programs46-56
  Arun Nampally, Cartic R. Ramakrishnan
  
• A Hybrid Approach to Inference in Probabilistic Non-Monotonic Logic Programming57-68
  Matthias Nickles, Alessandra Mileo
  
• The Distribution Semantics is Well-Defined for All Normal Programs69-84
  Fabrizio Riguzzi
  
• Probabilistic Abductive Logic Programming using Dirichlet Priors85-98
  Calin-Rares Turliuc, Luke Dickens, Alessandra Russo, Krysia Broda
  

Presented Papers which Appear Elsewhere

• Anytime Inference in Probabilistic Logic Programs with Tp-Compilation
  Jonas Vlasselaer, Guy Van den Broeck, Angelika Kimmig, Wannes Meert, Luc De Raedt
  International Joint Conference on Artificial Intelligence, 2015, AAAI Press / International Joint Conferences on Artificial Intelligence