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 82 articles , 1 standard article )

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  1. Belle, Vaishak; De Raedt, Luc: Semiring programming: a semantic framework for generalized sum product problems (2020)
  2. Belle, Vaishak; Levesque, Hector J.: Regression and progression in stochastic domains (2020)
  3. Bellodi, Elena; Alberti, Marco; Riguzzi, Fabrizio; Zese, Riccardo: MAP inference for probabilistic logic programming (2020)
  4. Cohen, William; Yang, Fan; Mazaitis, Kathryn Rivard: TensorLog: a probabilistic database implemented using deep-learning infrastructure (2020)
  5. Cropper, Andrew; Evans, Richard; Law, Mark: Inductive general game playing (2020)
  6. D’Asaro, Fabio Aurelio; Bikakis, Antonis; Dickens, Luke; Miller, Rob: Probabilistic reasoning about epistemic action narratives (2020)
  7. Kazemi, Seyed Mehran; Goel, Rishab; Jain, Kshitij; Kobyzev, Ivan; Sethi, Akshay; Forsyth, Peter; Poupart, Pascal: Representation learning for dynamic graphs: a survey (2020)
  8. Kolb, Samuel; Teso, Stefano; Dries, Anton; De Raedt, Luc: Predictive spreadsheet autocompletion with constraints (2020)
  9. Mantadelis, Theofrastos; Bistarelli, Stefano: Probabilistic abstract argumentation frameworks, a possible world view (2020)
  10. Balai, Evgenii; Gelfond, Michael; Zhang, Yuanlin: P-log: refinement and a new coherency condition (2019)
  11. Di Franco, Anthony: Information-gain computation in the \textscFifthsystem (2019)
  12. Guimarães, Victor; Paes, Aline; Zaverucha, Gerson: Online probabilistic theory revision from examples with ProPPR (2019)
  13. Nguembang Fadja, Arnaud; Riguzzi, Fabrizio: Lifted discriminative learning of probabilistic logic programs (2019)
  14. Sandra Dylus, Jan Christiansen, Finn Teegen: Implementing a Library for Probabilistic Programming using Non-strict Non-determinism (2019) arXiv
  15. Vieira de Faria, Francisco H. O.; Gusmão, Arthur Colombini; De Bona, Glauber; Mauá, Denis Deratani; Cozman, Fabio Gagliardi: Speeding up parameter and rule learning for acyclic probabilistic logic programs (2019)
  16. Wielemaker, Jan; Riguzzi, Fabrizio; Kowalski, Robert A.; Lager, Torbjörn; Sadri, Fariba; Calejo, Miguel: Using SWISH to realize interactive web-based tutorials for logic-based languages (2019)
  17. Abdallah, Samer: PRISM revisited: declarative implementation of a probabilistic programming language using multi-prompt delimited control (2018)
  18. Bain, Michael; Srinivasan, Ashwin: Identification of biological transition systems using meta-interpreted logic programs (2018)
  19. Belle, Vaishak; Levesque, Hector J.: Reasoning about discrete and continuous noisy sensors and effectors in dynamical systems (2018)
  20. Kojima, Ryosuke; Sato, Taisuke: Learning to rank in PRISM (2018)

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