ProbLog is a probabilistic logic programming language based on Prolog. Two ProbLog implementations are available, based on a different methodology and offering a different functionality. ProbLog1, or briefly ProbLog, focusses on computing the success probability of a given query, either exactly or using various approximate methods. ProbLog1 also supports parameter learning, in both the learning from entailment and learning from interpretations setting. ProbLog1 also supports decision-theoretic inference. ProbLog2 allows the user to compute marginal probabilities of any number of ground atoms in the presence of evidence (in comparison, the succes probability setting of ProbLog1 corresponds to having a single query and no evidence). ProbLog2 also supports parameter learning in the learning from interpretations setting.

References in zbMATH (referenced in 34 articles , 1 standard article )

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  1. Nitti, Davide; De Laet, Tinne; De Raedt, Luc: Probabilistic logic programming for hybrid relational domains (2016)
  2. De Raedt, Luc; Kimmig, Angelika: Probabilistic (logic) programming concepts (2015)
  3. Di Mauro, Nicola; Bellodi, Elena; Riguzzi, Fabrizio: Bandit-based Monte-Carlo structure learning of probabilistic logic programs (2015)
  4. Hescott, Benjamin J.; Khardon, Roni: The complexity of reasoning with FODD and GFODD (2015)
  5. Kimmig, Angelika; Mihalkova, Lilyana; Getoor, Lise: Lifted graphical models: a survey (2015)
  6. Michels, Steffen; Hommersom, Arjen; Lucas, Peter J.F.; Velikova, Marina: A new probabilistic constraint logic programming language based on a generalised distribution semantics (2015)
  7. Van Ranst, Wiebe; Vennekens, Joost: An OpenCL implementation of a forward sampling algorithm for CP-logic (2015)
  8. Wang, William Yang; Mazaitis, Kathryn; Lao, Ni; Cohen, William W.: Efficient inference and learning in a large knowledge base. Reasoning with extracted information using a locally groundable first-order probabilistic logic (2015)
  9. Frasconi, Paolo; Costa, Fabrizio; De Raedt, Luc; De Grave, Kurt: kLog: a language for logical and relational learning with kernels (2014)
  10. Marple, Kyle; Gupta, Gopal: Galliwasp: a goal-directed answer set solver (2013)
  11. Wang, Y.H.; Cao, K.; Zhang, X.M.: Complex event processing over distributed probabilistic event streams (2013)
  12. Finthammer, Marc; Thimm, Matthias: An integrated development environment for probabilistic relational reasoning (2012)
  13. Islam, Muhammad Asiful; Ramakrishnan, C.R.; Ramakrishnan, I.V.: Inference in probabilistic logic programs with continuous random variables (2012)
  14. Muggleton, Stephen; De Raedt, Luc; Poole, David; Bratko, Ivan; Flach, Peter: ILP turns 20. Biography and future challenges (2012)
  15. Natarajan, Sriraam; Khot, Tushar; Kersting, Kristian; Gutmann, Bernd; Shavlik, Jude: Gradient-based boosting for statistical relational learning: the relational dependency network case (2012)
  16. Renkens, Joris; Van Den Broeck, Guy; Nijssen, Siegfried: $k$-optimal: a novel approximate inference algorithm for ProbLog (2012)
  17. Riguzzi, Fabrizio; Di Mauro, Nicola: Applying the information bottleneck to statistical relational learning (2012)
  18. Yue, Anbu; Liu, Weiru; Hunter, Anthony: Imprecise probabilistic query answering using measures of ignorance and degree of satisfaction (2012)
  19. Gutmann, Bernd; Thon, Ingo; Kimmig, Angelika; Bruynooghe, Maurice; De Raedt, Luc: The magic of logical inference in probabilistic programming (2011)
  20. Kimmig, Angelika; Demoen, Bart; De Raedt, Luc; Costa, VĂ­tor Santos; Rocha, Ricardo: On the implementation of the probabilistic logic programming language ProbLog (2011)

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