Publications

Key publications

ProbLog2 System

  • D. Fierens, G. Van den Broeck, J. Renkens, D. Shterionov, B. Gutmann, I. Thon, G. Janssens and L. De Raedt. Inference and learning in probabilistic logic programs using weighted Boolean formulas. Theory and Practice of Logic Programming, 15:3, pp. 358 - 401, Cambridge University Press, 2015. PDF DOI

ProbLog Language

  • L. De Raedt and A. Kimmig. Probabilistic (logic) programming concepts. Machine Learning, 100:1, pp. 5 - 47, Springer New York LLC, 2015. PDF DOI
  • L. De Raedt, A. Kimmig and H. Toivonen. ProbLog: A probabilistic Prolog and its application in link discovery. IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence, pp. 2462 - 2467, 2007. PDF

Tutorial Slides

  • L. De Raedt and A. Kimmig. Probabilistic programming (IJCAI tutorial). IJCAI, 2015. PDF
  • L. De Raedt and A. Kimmig. Probabilistic programming (ECAI tutorial). ECAI, 2014. PDF
  • A. Kimmig. Probabilistic programming (KI tutorial). KI, 2014. PDF

Language

  • L. De Raedt and A. Kimmig. Probabilistic (logic) programming concepts. Machine Learning, 100:1, pp. 5 - 47, Springer New York LLC, 2015. PDF DOI
  • D. Fierens, G. Van den Broeck, M. Bruynooghe and L. De Raedt. Constraints for probabilistic logic programming. Proceedings of the NIPS Probabilistic Programming Workshop, pp. 1 - 4, 2012. PDF
  • A. Kimmig, G. Van den Broeck and L. De Raedt. An algebraic Prolog for reasoning about possible worlds. Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, pp. 209 - 214, AAAI Press, 2011. PDF
  • M. Bruynooghe, T. Mantadelis, A. Kimmig, B. Gutmann, J. Vennekens, G. Janssens and L. De Raedt. ProbLog technology for inference in a probabilistic first order logic. ECAI 2010 - 19th European Conference on Artificial Intelligence, pp. 719 - 724, IOS Press, 2010. PDF DOI
  • L. De Raedt, A. Kimmig, B. Gutmann, K. Kersting, V. Santos Costa and H. Toivonen. Probabilistic inductive querying using ProbLog. Inductive Databases and Constraint-Based Data Mining, Springer, 2010. PDF DOI TR
  • G. Van den Broeck, I. Thon, M. van Otterlo and L. De Raedt. DTProbLog: A decision-theoretic probabilistic Prolog. Proceedings of the twenty-fourth AAAI conference on artificial intelligence, pp. 1217 - 1222, AAAI Press, 2010. PDF
  • L. De Raedt, B. Demoen, D. Fierens, B. Gutmann, G. Janssens, A. Kimmig, N. Landwehr, T. Mantadelis, W. Meert, R. Rocha, V. Santos Costa, I. Thon and J. Vennekens. Towards digesting the alphabet-soup of statistical relational learning. NIPS 2008 Workshop on Probabilistic Programming, 2008. PDF
  • L. De Raedt, A. Kimmig and H. Toivonen. ProbLog: A probabilistic Prolog and its application in link discovery. IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence, pp. 2462 - 2467, 2007. PDF

Systems

ProbLog2

  • A. Dries, A. Kimmig, W. Meert, J. Renkens, G. Van den Broeck, J. Vlasselaer and L. De Raedt. ProbLog2: Probabilistic logic programming. Lecture Notes in Computer Science, 9286, pp. 312 - 315, Springer, 2015. PDF DOI
  • D. Fierens, G. Van den Broeck, J. Renkens, D. Shterionov, B. Gutmann, I. Thon, G. Janssens and L. De Raedt. Inference and learning in probabilistic logic programs using weighted Boolean formulas. Theory and Practice of Logic Programming, 15:3, pp. 358 - 401, Cambridge University Press, 2015. PDF DOI
  • J. Renkens, D. Shterionov, G. Van den Broeck, J. Vlasselaer, D. Fierens, W. Meert, G. Janssens and L. De Raedt. ProbLog2: From probabilistic programming to statistical relational learning. Proceedings of the NIPS Probabilistic Programming Workshop, 2012. PDF
  • D. Fierens, G. Van den Broeck, I. Thon, B. Gutmann and L. De Raedt. Inference in probabilistic logic programs using weighted CNF's. Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI), pp. 211 - 220, 2011. PDF TR

ProbLog1

  • A. Kimmig, B. Demoen, L. De Raedt, V. Santos Costa and R. Rocha. On the implementation of the probabilistic logic programming language ProbLog. Theory and Practice of Logic Programming, 11, pp. 235 - 262, Cambridge University Press, 2011. PDF DOI
  • A. Kimmig, V. Santos Costa, R. Rocha, B. Demoen and L. De Raedt. On the efficient execution of ProbLog programs. International Conference on Logic Programming, LNCS, volume 5366, pp. 175 - 189, Springer, 2008. PDF DOI
  • L. De Raedt, A. Kimmig and H. Toivonen. ProbLog: A probabilistic Prolog and its application in link discovery. IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence, pp. 2462 - 2467, 2007. PDF

Inference

  • J. Vlasselaer, G. Van den Broeck, A. Kimmig, W. Meert and L. De Raedt. Anytime inference in probabilistic logic programs with Tp-compilation. Proceedings of 24th International Joint Conference on Artificial Intelligence (IJCAI), pp. 1852 - 1858, 2015. PDF
  • D. Shterionov, J. Renkens, J. Vlasselaer, A. Kimmig, W. Meert and G. Janssens. The most probable explanation for probabilistic logic programs with annotated disjunctions. Inductive Logic Programming, 2015. PDF
  • J. Renkens, A. Kimmig and L. De Raedt. Lazy explanation-based approximation for probabilistic logic programming. Proceedings of the StarAI workshop at UAI, 2015. PDF
  • J. Vlasselaer, J. Renkens, G. Van den Broeck and L. De Raedt. Compiling probabilistic logic programs into sentential decision diagrams. Proceedings Workshop on Probabilistic Logic Programming (PLP), pp. 1 - 10, 2014. PDF
  • J. Renkens, A. Kimmig, G. Van den Broeck and L. De Raedt. Explanation-based approximate weighted model counting for probabilistic logics. Proceedings of the 28th AAAI Conference on Artificial Intelligence, pp. 2490 - 2496, 2014. PDF
  • D. Shterionov, T. Mantadelis and G. Janssens. Pattern-based compaction for ProbLog inference. Technical Report, Department of Computer Science, KU Leuven, 2013. PDF
  • B. Moldovan, I. Thon, J. Davis and L. De Raedt. MCMC estimation of conditional probabilities in probabilistic programming languages. Lecture Notes in Computer Science, volume 7958, pp. 436 - 448, Springer, 2013. PDF DOI
  • J. Renkens, G. Van den Broeck and S. Nijssen. k-optimal: A novel approximate inference algorithm for ProbLog. Machine Learning, 89:3, pp. 215 - 231, Springer New York LLC, 2012. PDF DOI
  • T. Mantadelis and G. Janssens. Nesting probabilistic inference. CICLOPS, pp. 1 - 16, 2011. PDF
  • D. Fierens, G. Van den Broeck, I. Thon, B. Gutmann and L. De Raedt. Inference in probabilistic logic programs using weighted CNF's. Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI), pp. 211 - 220, 2011. PDF TR
  • T. Mantadelis, R. Rocha, A. Kimmig and G. Janssens. Preprocessing Boolean formulae for BDDs in a probabilistic context. Logics in Artificial Intelligence, 12th European Conference, JELIA 2010, Proceedings, volume 6341, pp. 260 - 272, Springer, 2010. PDF DOI
  • D. Shterionov, A. Kimmig, T. Mantadelis and G. Janssens. DNF sampling for ProbLog inference. Proceedings International Colloquium on Implementation of Constraint and LOgic Programming Systems (CICLOPS), 2010. PDF
  • T. Mantadelis and G. Janssens. Dedicated tabling for a probabilistic setting. Technical Communications of the 26th International Conference on Logic Programming, volume 7, pp. 124 - 133, Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, 2010. PDF DOI
  • T. Mantadelis and G. Janssens. Variable compression in ProbLog. Lecture Notes in Computer Science, volume 6397, pp. 504 - 518, Springer-Verlag's, 2010. PDF DOI TR
  • A. Kimmig, B. Demoen, L. De Raedt, V. Santos Costa and R. Rocha. On the implementation of the probabilistic logic programming language ProbLog. Theory and Practice of Logic Programming, 11, pp. 235 - 262, Cambridge University Press, 2011. PDF DOI
  • A. Kimmig, V. Santos Costa, R. Rocha, B. Demoen and L. De Raedt. On the efficient execution of ProbLog programs. International Conference on Logic Programming, LNCS, volume 5366, pp. 175 - 189, Springer, 2008. PDF DOI
  • L. De Raedt, A. Kimmig and H. Toivonen. ProbLog: A probabilistic Prolog and its application in link discovery. IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence, pp. 2462 - 2467, 2007. PDF

Learning

  • L. De Raedt, A. Dries, I. Thon, G. Van den Broeck and M. Verbeke. Inducing probabilistic relational rules from probabilistic examples. Proceedings of 24th International Joint Conference on Artificial Intelligence (IJCAI), 2015. PDF
  • B. Gutmann, I. Thon and L. De Raedt. Learning the parameters of probabilistic logic programs from interpretations. Machine Learning and Knowledge Discovery in Databases, volume 6911, pp. 581 - 596, Springer, 2011. PDF DOI TR
  • A. Kimmig and L. De Raedt. Local query mining in a probabilistic Prolog. Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence (IJCAI-09), volume 2, pp. 1095 - 1100, AAAI Press, 2009. PDF
  • B. Gutmann, A. Kimmig, K. Kersting and L. De Raedt. Parameter learning in probabilistic databases: A least squares approach. Machine Learning and Knowledge Discovery in Databases, volume 5211, pp. 473 - 488, Springer Verlag, 2008. PDF DOI TR
  • L. De Raedt, K. Kersting, A. Kimmig, K. Revoredo and H. Toivonen. Compressing probabilistic Prolog programs. Machine learning, 70:2-3, pp. 151 - 168, Kluwer Academic Publishers, 2008. PDF DOI
  • A. Kimmig, L. De Raedt and H. Toivonen. Probabilistic explanation based learning. Lecture notes in computer science, volume 4701, pp. 176 - 187, Springer, 2007. PDF DOI

Distributional Clauses and Continuous Distributions

  • D. Nitti, T. De Laet and L. De Raedt. Relational object tracking and learning. 2014 IEEE International Conference on Robotics and Automation, pp. 935 - 942, 2014. PDF DOI
  • D. Nitti, T. De Laet and L. De Raedt. A particle filter for hybrid relational domains. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013, pp. 2764 - 2771, IEEE, 2013. PDF DOI
  • B. Gutmann, I. Thon, A. Kimmig, M. Bruynooghe and L. De Raedt. The magic of logical inference in probabilistic programming. Theory and Practice of Logic Programming, 11, pp. 663 - 680, Cambridge University Press, 2011. PDF DOI
  • B. Gutmann, M. Jaeger and L. De Raedt. Extending ProbLog with continuous distributions. Inductive Logic Programming, volume 6489, pp. 76 - 91, Springer, 2010. PDF DOI

Decision-Theoretic ProbLog

  • G. Van den Broeck, I. Thon, M. van Otterlo and L. De Raedt. DTProbLog: A decision-theoretic probabilistic Prolog. Proceedings of the twenty-fourth AAAI conference on artificial intelligence, pp. 1217 - 1222, AAAI Press, 2010. PDF
  • I. Thon, B. Gutmann and G. Van den Broeck. Probabilistic programming for planning problems. Statistical Relational AI workshop, 2010. PDF

Dynamic Models

  • J. Vlasselaer, W. Meert, G. Van den Broeck and L. De Raedt. Exploiting local and repeated structure in dynamic Bayesian networks. Artificial Intelligence, North-Holland Pub. Co., 2015. PDF

Ph.D. Dissertations

  • B. Moldovan. Relational Affordances and their Applications. PhD Thesis, KU Leuven, 2015. PDF
  • D. Shterionov. Design and Development of Probabilistic Inference Pipelines. PhD Thesis, KU Leuven, 2015. PDF
  • T. Mantadelis. Efficient Algorithms for Prolog Based Probabilistic Logic Programming (Efficiënte algoritmen voor prolog gebaseerd probabilistisch logisch programmeren). PhD Thesis, KU Leuven, 2012. PDF
  • I. Thon. Stochastic Relational Processes and Models: Learning and Reasoning (Stochastisch relationele processen en modellen: leren en redeneren). PhD Thesis, KU Leuven, 2011. PDF
  • B. Gutmann. On Continuous Distributions and Parameter Estimation in Probabilistic Logic Programs (Over continue verdelingen en het schatten van parameters in probabilistische logische programma's). PhD Thesis, KU Leuven, 2011. PDF
  • A. Kimmig. A Probabilistic Prolog and its Applications (Een probabilistische prolog en zijn toepassingen). PhD Thesis, KU Leuven, 2010. PDF

Datasets and Applications

  • B. Aerts, T. Goedemé and J. Vennekens. A Probabilistic Logic Programming Approach to Automatic Video Montage. ECAI 2016: proceedings, volume 285, pp. 234 - 242, IOS Press, 2016. PDF DOI
  • A. Dries. Declarative data generation with ProbLog. International symposium on information and communication technology, 2015. PDF
  • W. Meert, A. Dries and A. Kimmig. WHIRL in ProbLog. Proceedings of the StarAI workshop at UAI, pp. 5 - 5, 2015. PDF
  • D. De Maeyer, L. Cloots, J. Renkens, L. De Raedt and K. Marchal. PheNetic: Network-based interpretation of unstructured gene lists in E. coli. Molecular Biosystems, 9:7, pp. 1594 - 1603, Royal Society of Chemistry, 2013. PDF DOI
  • A. Skarlatidis, A. Artikis, J. Filippou and G. Paliouras. A Probabilistic Logic Programming Event Calculus. Theory and Practice of Logic Programming, 15:2, 2013. PDF
  • B. Moldovan, P. Moreno, M. van Otterlo, J. Santos-Victor and L. De Raedt. Learning relational affordance models for robots in multi-object manipulation tasks. IEEE International Conference on Robotics and Automation, ICRA 2012, pp. 4373 - 4378, 2012. PDF DOI
  • B. Moldovan, M. van Otterlo, P. Moreno, J. Santos-Victor and L. De Raedt. Statistical relational learning of object aff ordances for robotic manipulation. Latest advances in inductive logic programming, Imperial College Press, 2012. PDF
  • B. Moldovan, L. Antanas and M. Hoffmann. Opening doors: An initial SRL approach. Preliminary Proceedings of the International Conference on Inductive Logic Programming, pp. 1 - 6, 2012. PDF
  • J. Vlasselaer and W. Meert. Statistical relational learning for prognostics. Proceedings of the 21st Belgian-Dutch Conference on Machine Learning, pp. 45 - 50, 2012. PDF
  • A. Kimmig and F. Costa. Link and node prediction in metabolic networks with probabilistic logic. Lecture Notes in Artificial Intelligence volume, 7250, Bisociative Knowledge Discovery, pp. 407 - 426, Springer, 2012. PDF DOI