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 41 articles , 1 standard article )

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  1. Ceylan, İsmail İlkan; Peñaloza, Rafael: The Bayesian ontology language $\mathcal BEL$ (2017)
  2. Nitti, Davide; De Laet, Tinne; De Raedt, Luc: Probabilistic logic programming for hybrid relational domains (2016)
  3. Orsini, Francesco; Frasconi, Paolo; De Raedt, Luc: kProbLog: an algebraic Prolog for kernel programming (2016)
  4. Riguzzi, Fabrizio: The distribution semantics for normal programs with function symbols (2016)
  5. Serra, Edoardo; Spezzano, Francesca; Subrahmanian, V.S.: ChoiceGAPs: competitive diffusion as a massive multi-player game in social networks (2016)
  6. Turliuc, Calin Rares; Dickens, Luke; Russo, Alessandra; Broda, Krysia: Probabilistic abductive logic programming using Dirichlet priors (2016)
  7. Vlasselaer, Jonas; Van den Broeck, Guy; Kimmig, Angelika; Meert, Wannes; De Raedt, Luc: $T_\mathcalP$-compilation for inference in probabilistic logic programs (2016)
  8. De Raedt, Luc; Kimmig, Angelika: Probabilistic (logic) programming concepts (2015)
  9. Di Mauro, Nicola; Bellodi, Elena; Riguzzi, Fabrizio: Bandit-based Monte-Carlo structure learning of probabilistic logic programs (2015)
  10. Hescott, Benjamin J.; Khardon, Roni: The complexity of reasoning with FODD and GFODD (2015)
  11. Kimmig, Angelika; Mihalkova, Lilyana; Getoor, Lise: Lifted graphical models: a survey (2015)
  12. Michels, Steffen; Hommersom, Arjen; Lucas, Peter J.F.; Velikova, Marina: A new probabilistic constraint logic programming language based on a generalised distribution semantics (2015)
  13. Van Ranst, Wiebe; Vennekens, Joost: An OpenCL implementation of a forward sampling algorithm for CP-logic (2015)
  14. 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)
  15. Frasconi, Paolo; Costa, Fabrizio; De Raedt, Luc; De Grave, Kurt: kLog: a language for logical and relational learning with kernels (2014)
  16. Marple, Kyle; Gupta, Gopal: Galliwasp: a goal-directed answer set solver (2013)
  17. Wang, Y.H.; Cao, K.; Zhang, X.M.: Complex event processing over distributed probabilistic event streams (2013)
  18. Finthammer, Marc; Thimm, Matthias: An integrated development environment for probabilistic relational reasoning (2012)
  19. Islam, Muhammad Asiful; Ramakrishnan, C.R.; Ramakrishnan, I.V.: Inference in probabilistic logic programs with continuous random variables (2012)
  20. Muggleton, Stephen; De Raedt, Luc; Poole, David; Bratko, Ivan; Flach, Peter: ILP turns 20. Biography and future challenges (2012)

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