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 64 articles , 1 standard article )

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  1. Di Franco, Anthony: Information-gain computation in the \textscFifthsystem (2019)
  2. Abdallah, Samer: PRISM revisited: declarative implementation of a probabilistic programming language using multi-prompt delimited control (2018)
  3. Bain, Michael; Srinivasan, Ashwin: Identification of biological transition systems using meta-interpreted logic programs (2018)
  4. Belle, Vaishak; Levesque, Hector J.: Reasoning about discrete and continuous noisy sensors and effectors in dynamical systems (2018)
  5. Kojima, Ryosuke; Sato, Taisuke: Learning to rank in PRISM (2018)
  6. Law, Mark; Russo, Alessandra; Broda, Krysia: The complexity and generality of learning answer set programs (2018)
  7. Buchman, David; Poole, David: Negative probabilities in probabilistic logic programs (2017)
  8. Ceylan, İsmail İlkan; Peñaloza, Rafael: The Bayesian ontology language (\mathcalBEL) (2017)
  9. Nitti, Davide; Belle, Vaishak; De Laet, Tinne; De Raedt, Luc: Planning in hybrid relational mdps (2017)
  10. Orsini, Francesco; Frasconi, Paolo; De Raedt, Luc: kProbLog: an algebraic prolog for machine learning (2017)
  11. 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)
  12. Riguzzi, Fabrizio; Cota, Giuseppe; Bellodi, Elena; Zese, Riccardo: Causal inference in cplint (2017)
  13. Schoenfisch, Joerg; Stuckenschmidt, Heiner: Analyzing real-world SPARQL queries and ontology-based data access in the context of probabilistic data (2017)
  14. Teso, Stefano; Sebastiani, Roberto; Passerini, Andrea: Structured learning modulo theories (2017)
  15. Cota, Giuseppe; Zese, Riccardo; Bellodi, Elena; Riguzzi, Fabrizio; Lamma, Evelina: Distributed parameter learning for probabilistic ontologies (2016)
  16. Kiselyov, Oleg: Probabilistic programming language and its incremental evaluation (2016)
  17. Nickles, Matthias: A tool for probabilistic reasoning based on logic programming and first-order theories under stable model semantics (2016)
  18. Nitti, Davide; De Laet, Tinne; De Raedt, Luc: Probabilistic logic programming for hybrid relational domains (2016)
  19. Orsini, Francesco; Frasconi, Paolo; De Raedt, Luc: kProbLog: an algebraic Prolog for kernel programming (2016)
  20. Riguzzi, Fabrizio: The distribution semantics for normal programs with function symbols (2016)

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