ProbLog

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 51 articles , 1 standard article )

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  1. Buchman, David; Poole, David: Negative probabilities in probabilistic logic programs (2017)
  2. Ceylan, İsmail İlkan; Peñaloza, Rafael: The Bayesian ontology language $\mathcal BEL$ (2017)
  3. Riguzzi, Fabrizio; Bellodi, Elena; Zese, Riccardo; Cota, Giuseppe; Lamma, Evelina: A survey of lifted inference approaches for probabilistic logic programming under the distribution semantics (2017)
  4. Riguzzi, Fabrizio; Cota, Giuseppe; Bellodi, Elena; Zese, Riccardo: Causal inference in cplint (2017)
  5. Schoenfisch, Joerg; Stuckenschmidt, Heiner: Analyzing real-world SPARQL queries and ontology-based data access in the context of probabilistic data (2017)
  6. Teso, Stefano; Sebastiani, Roberto; Passerini, Andrea: Structured learning modulo theories (2017)
  7. Cota, Giuseppe; Zese, Riccardo; Bellodi, Elena; Riguzzi, Fabrizio; Lamma, Evelina: Distributed parameter learning for probabilistic ontologies (2016)
  8. Kiselyov, Oleg: Probabilistic programming language and its incremental evaluation (2016)
  9. Nickles, Matthias: A tool for probabilistic reasoning based on logic programming and first-order theories under stable model semantics (2016)
  10. Nitti, Davide; De Laet, Tinne; De Raedt, Luc: Probabilistic logic programming for hybrid relational domains (2016)
  11. Orsini, Francesco; Frasconi, Paolo; De Raedt, Luc: kProbLog: an algebraic Prolog for kernel programming (2016)
  12. Riguzzi, Fabrizio: The distribution semantics for normal programs with function symbols (2016)
  13. Serra, Edoardo; Spezzano, Francesca; Subrahmanian, V.S.: ChoiceGAPs: competitive diffusion as a massive multi-player game in social networks (2016)
  14. Turliuc, Calin Rares; Dickens, Luke; Russo, Alessandra; Broda, Krysia: Probabilistic abductive logic programming using Dirichlet priors (2016)
  15. Vlasselaer, Jonas; Van den Broeck, Guy; Kimmig, Angelika; Meert, Wannes; De Raedt, Luc: $T_\mathcalP$-compilation for inference in probabilistic logic programs (2016)
  16. De Raedt, Luc; Kimmig, Angelika: Probabilistic (logic) programming concepts (2015)
  17. Di Mauro, Nicola; Bellodi, Elena; Riguzzi, Fabrizio: Bandit-based Monte-Carlo structure learning of probabilistic logic programs (2015)
  18. Hescott, Benjamin J.; Khardon, Roni: The complexity of reasoning with FODD and GFODD (2015)
  19. Kimmig, Angelika; Mihalkova, Lilyana; Getoor, Lise: Lifted graphical models: a survey (2015)
  20. Michels, Steffen; Hommersom, Arjen; Lucas, Peter J.F.; Velikova, Marina: A new probabilistic constraint logic programming language based on a generalised distribution semantics (2015)

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