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 99 articles , 1 standard article )

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  1. Azzolini, Damiano; Bellodi, Elena; Ferilli, Stefano; Riguzzi, Fabrizio; Zese, Riccardo: Abduction with probabilistic logic programming under the distribution semantics (2022)
  2. Doleschal, Johannes; Kimelfeld, Benny; Martens, Wim; Peterfreund, Liat: Weight annotation in information extraction (2022)
  3. Latour, Anna L. D.; Babaki, Behrouz; Fokkinga, Daniël; Anastacio, Marie; Hoos, Holger H.; Nijssen, Siegfried: Exact stochastic constraint optimisation with applications in network analysis (2022)
  4. Stehr, Mark-Oliver; Kim, Minyoung; Talcott, Carolyn L.: A probabilistic approximate logic for neuro-symbolic learning and reasoning (2022)
  5. Artikis, Alexander; Makris, Evangelos; Paliouras, Georgios: A probabilistic interval-based event calculus for activity recognition (2021)
  6. Azzolini, Damiano; Riguzzi, Fabrizio: Optimizing probabilities in probabilistic logic programs (2021)
  7. Azzolini, Damiano; Riguzzi, Fabrizio; Lamma, Evelina: A semantics for hybrid probabilistic logic programs with function symbols (2021)
  8. Bellodi, Elena; Gavanelli, Marco; Zese, Riccardo; Lamma, Evelina; Riguzzi, Fabrizio: Nonground abductive logic programming with probabilistic integrity constraints (2021)
  9. Ceylan, İsmail İlkan; Darwiche, Adnan; Van den Broeck, Guy: Open-world probabilistic databases: semantics, algorithms, complexity (2021)
  10. Gu, Tao; Zanasi, Fabio: Coalgebraic semantics for probabilistic logic programming (2021)
  11. Manhaeve, Robin; Dumančić, Sebastijan; Kimmig, Angelika; Demeester, Thomas; De Raedt, Luc: Neural probabilistic logic programming in DeepProbLog (2021)
  12. Nguembang Fadja, Arnaud; Riguzzi, Fabrizio; Lamma, Evelina: Learning hierarchical probabilistic logic programs (2021)
  13. Riguzzi, Fabrizio; Bellodi, Elena; Zese, Riccardo; Alberti, Marco; Lamma, Evelina: Probabilistic inductive constraint logic (2021)
  14. Šourek, Gustav; Železný, Filip; Kuželka, Ondřej: Beyond graph neural networks with lifted relational neural networks (2021)
  15. Wang, Bin; Shen, Jun; Zhang, Shutao; Zhang, Zhizheng: On the strong equivalences for (\mathrmLP^MLN) programs (2021)
  16. Belle, Vaishak; De Raedt, Luc: Semiring programming: a semantic framework for generalized sum product problems (2020)
  17. Belle, Vaishak; Levesque, Hector J.: Regression and progression in stochastic domains (2020)
  18. Bellodi, Elena; Alberti, Marco; Riguzzi, Fabrizio; Zese, Riccardo: MAP inference for probabilistic logic programming (2020)
  19. Cohen, William; Yang, Fan; Mazaitis, Kathryn Rivard: TensorLog: a probabilistic database implemented using deep-learning infrastructure (2020)
  20. Cropper, Andrew; Evans, Richard; Law, Mark: Inductive general game playing (2020)

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