References

  1. Marco Alberti, Elena Bellodi, Giuseppe Cota, Fabrizio Riguzzi & Riccardo Zese (2017): cplint on SWISH: Probabilistic Logical Inference with a Web Browser. Intelligenza Artificiale 11(1), pp. 47–64, doi:10.3233/IA-170105.
  2. Damiano Azzolini, Fabrizio Riguzzi & Evelina Lamma (2019): Studying Transaction Fees in the Bitcoin Blockchain with Probabilistic Logic Programming. Information 10(11), pp. 335, doi:10.3390/info10110335.
  3. Damiano Azzolini, Fabrizio Riguzzi & Evelina Lamma (2021): A Semantics for Hybrid Probabilistic Logic Programs with Function Symbols. Artificial Intelligence 294, pp. 103452, doi:10.1016/j.artint.2021.103452.
  4. Damiano Azzolini, Fabrizio Riguzzi, Evelina Lamma & Franco Masotti (2019): A Comparison of MCMC Sampling for Probabilistic Logic Programming. In: Mario Alviano, Gianluigi Greco & Francesco Scarcello: Proceedings of the 18th Conference of the Italian Association for Artificial Intelligence (AI*IA2019), Rende, Italy 19-22 November 2019, Lecture Notes in Computer Science. Springer, Heidelberg, Germany, doi:10.1007/978-3-030-35166-3_2.
  5. Vaishak Belle (2020): Symbolic Logic Meets Machine Learning: A Brief Survey in Infinite Domains. In: Jesse Davis & Karim Tabia: Scalable Uncertainty Management. Springer International Publishing, Cham, pp. 3–16, doi:10.1007/978-3-030-58449-8_1.
  6. Y.S. Chow & H. Teicher (2012): Probability Theory: Independence, Interchangeability, Martingales. Springer Texts in Statistics. Springer.
  7. Fabio Gagliardi Cozman & Denis Deratani Mauá (2017): On the Semantics and Complexity of Probabilistic Logic Programs. Journal of Artificial Intelligence Research 60, pp. 221–262, doi:10.1613/jair.5482.
  8. Luc De Raedt & Angelika Kimmig (2015): Probabilistic (Logic) Programming Concepts. Machine Learning 100(1), pp. 5–47, doi:10.1007/s10994-015-5494-z.
  9. Luc De Raedt, Angelika Kimmig & Hannu Toivonen (2007): ProbLog: A Probabilistic Prolog and Its Application in Link Discovery. In: Manuela M. Veloso: 20th International Joint Conference on Artificial Intelligence (IJCAI 2007) 7. AAAI Press/IJCAI, pp. 2462–2467. Available at http://www.ijcai.org/papers07/Papers/IJCAI07-396.pdf.
  10. Bernd Gutmann, Manfred Jaeger & Luc De Raedt (2011): Extending ProbLog with Continuous Distributions. In: Paolo Frasconi & Francesca A. Lisi: 20th International Conference on Inductive Logic Programming (ILP 2010), LNCS 6489. Springer, pp. 76–91, doi:10.1007/978-3-642-21295-6_12.
  11. Muhammad Asiful Islam, CR Ramakrishnan & IV Ramakrishnan (2012): Inference in probabilistic logic programs with continuous random variables. Theory and Practice of Logic Programming 12, pp. 505–523, doi:10.1017/S1471068412000154.
  12. Steffen Michels, Arjen Hommersom, Peter J. F. Lucas & Marina Velikova (2015): A new probabilistic constraint logic programming language based on a generalised distribution semantics. Artificial Intelligence 228, pp. 1–44, doi:10.1016/j.artint.2015.06.008.
  13. Stephen Muggleton (2000): Learning Stochastic Logic Programs. In: Lise Getoor & David Jensen: Learning Statistical Models from Relational Data, Papers from the 2000 AAAI Workshop, AAAI Workshops WS-00-06. AAAI Press, pp. 36–41.
  14. Arnaud Nguembang Fadja & Fabrizio Riguzzi (2017): Probabilistic Logic Programming in Action. In: Andreas Holzinger, Randy Goebel, Massimo Ferri & Vasile Palade: Towards Integrative Machine Learning and Knowledge Extraction, LNCS 10344. Springer, doi:10.1007/978-3-319-69775-8_5.
  15. David Poole (1997): The Independent Choice Logic for Modelling Multiple Agents Under Uncertainty. Artificial Intelligence 94(1-2), pp. 7–56, doi:10.1016/S0004-3702(97)00027-1.
  16. Fabrizio Riguzzi (2013): MCINTYRE: A Monte Carlo System for Probabilistic Logic Programming. Fundamenta Informaticae 124(4), pp. 521–541, doi:10.3233/FI-2013-847.
  17. Fabrizio Riguzzi (2016): The Distribution Semantics for Normal Programs with Function Symbols. International Journal of Approximate Reasoning 77, pp. 1–19, doi:10.1016/j.ijar.2016.05.005.
  18. Fabrizio Riguzzi (2018): Foundations of Probabilistic Logic Programming. River Publishers, Gistrup, Denmark.
  19. Fabrizio Riguzzi, Elena Bellodi, Evelina Lamma, Riccardo Zese & Giuseppe Cota (2016): Probabilistic Logic Programming on the Web. Software: Practice and Experience 46(10), pp. 1381–1396, doi:10.1002/spe.2386.
  20. Fabrizio Riguzzi & Terrance Swift (2013): Well–Definedness and Efficient Inference for Probabilistic Logic Programming under the Distribution Semantics. Theory and Practice of Logic Programming 13(2), pp. 279–302, doi:10.1017/S1471068411000664.
  21. Taisuke Sato (1995): A Statistical Learning Method for Logic Programs with Distribution Semantics. In: Leon Sterling: Logic Programming, Proceedings of the Twelfth International Conference on Logic Programming, Tokyo, Japan, June 13-16, 1995. MIT Press, pp. 715–729.
  22. A. Van Gelder, K. A. Ross & J. S. Schlipf (1991): The Well-founded Semantics for General Logic Programs. Journal of the ACM 38(3), pp. 620–650, doi:10.1145/116825.116838.
  23. J. Vennekens, Marc Denecker & Maurice Bruynooghe (2009): CP-logic: A language of causal probabilistic events and its relation to logic programming. Theory and Practice of Logic Programming 9(3), pp. 245–308, doi:10.1017/S1471068409003767.
  24. Joost Vennekens, Sofie Verbaeten & Maurice Bruynooghe (2004): Logic Programs With Annotated Disjunctions. In: Bart Demoen & Vladimir Lifschitz: 20th International Conference on Logic Programming (ICLP 2004), LNCS 3131. Springer, pp. 431–445, doi:10.1007/978-3-540-27775-0_30.
  25. Pedro Zuidberg Dos Martires, Anton Dries & Luc De Raedt (2018): Knowledge Compilation with Continuous Random Variables and its Application in Hybrid Probabilistic Logic Programming. CoRR abs/1807.00614.

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