@book {Judea88,
    AUTHOR = {Pearl, Judea},
     TITLE = {Probabilistic reasoning in intelligent systems: networks of
              plausible inference},
    SERIES = {The Morgan Kaufmann Series in Representation and Reasoning},
 PUBLISHER = {Morgan Kaufmann, San Mateo, CA},
      YEAR = {1988},
     PAGES = {xx+552},
      ISBN = {0-934613-73-7},
   MRCLASS = {68-02 (68T01 92A25)},
  MRNUMBER = {965765},
MRREVIEWER = {V. Yu. Trakhtman},
}

 @article{sympy,
     title = {SymPy: symbolic computing in Python},
     author = {Meurer, Aaron and Smith, Christopher P. and Paprocki, Mateusz and \v{C}ert\'{i}k, Ond\v{r}ej and Kirpichev, Sergey B. and Rocklin, Matthew and Kumar, AMiT and Ivanov, Sergiu and Moore, Jason K. and Singh, Sartaj and Rathnayake, Thilina and Vig, Sean and Granger, Brian E. and Muller, Richard P. and Bonazzi, Francesco and Gupta, Harsh and Vats, Shivam and Johansson, Fredrik and Pedregosa, Fabian and Curry, Matthew J. and Terrel, Andy R. and Rou\v{c}ka, \v{S}t\v{e}p\'{a}n and Saboo, Ashutosh and Fernando, Isuru and Kulal, Sumith and Cimrman, Robert and Scopatz, Anthony},
     year = 2017,
     month = jan,
     keywords = {Python, Computer algebra system, Symbolics},
     abstract = {
                SymPy is an open source computer algebra system written in pure Python. It is built with a focus on extensibility and ease of use, through both interactive and programmatic applications. These characteristics have led SymPy to become a popular symbolic library for the scientific Python ecosystem. This paper presents the architecture of SymPy, a description of its features, and a discussion of select submodules. The supplementary material provide additional examples and further outline details of the architecture and features of SymPy.
             },
     volume = 3,
     pages = {e103},
     journal = {PeerJ Computer Science},
     issn = {2376-5992},
     url = {https://doi.org/10.7717/peerj-cs.103},
     doi = {10.7717/peerj-cs.103}
    }

@inproceedings{verreet2022inference,
  title={Inference and learning with model uncertainty in probabilistic logic programs},
  author={Verreet, Victor and Derkinderen, Vincent and Dos Martires, Pedro Zuidberg and De Raedt, Luc},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={36},
  number={9},
  pages={10060--10069},
  year={2022}
}

@article{cropper2022inductive,
  title={Inductive logic programming at 30},
  author={Cropper, Andrew and Duman{\v{c}}i{\'c}, Sebastijan and Evans, Richard and Muggleton, Stephen H},
  journal={Machine Learning},
  volume={111},
  number={1},
  pages={147--172},
  year={2022},
  publisher={Springer}
}

@article{cozman2020joy,
  title={The joy of probabilistic answer set programming: semantics, complexity, expressivity, inference},
  author={Cozman, Fabio Gagliardi and Mau{\'a}, Denis Deratani},
  journal={International Journal of Approximate Reasoning},
  volume={125},
  pages={218--239},
  year={2020},
  publisher={Elsevier}
}

@book{riguzzi2022foundations,
  title={Foundations of probabilistic logic programming: Languages, semantics, inference and learning},
  author={Riguzzi, Fabrizio},
  year={2022},
  publisher={CRC Press}
}

@article{gebser2012answer,
  title={Answer set solving in practice},
  author={Gebser, Martin and Kaminski, Roland and Kaufmann, Benjamin and Schaub, Torsten},
  journal={Synthesis lectures on artificial intelligence and machine learning},
  volume={6},
  number={3},
  pages={1--238},
  year={2012},
  publisher={Morgan \& Claypool Publishers}
}

@article{10.7717/peerj-cs.103,
     title = {SymPy: symbolic computing in Python},
     author = {Meurer, Aaron and Smith, Christopher P. and Paprocki, Mateusz and \v{C}ert\'{i}k, Ond\v{r}ej and Kirpichev, Sergey B. and Rocklin, Matthew and Kumar, AMiT and Ivanov, Sergiu and Moore, Jason K. and Singh, Sartaj and Rathnayake, Thilina and Vig, Sean and Granger, Brian E. and Muller, Richard P. and Bonazzi, Francesco and Gupta, Harsh and Vats, Shivam and Johansson, Fredrik and Pedregosa, Fabian and Curry, Matthew J. and Terrel, Andy R. and Rou\v{c}ka, \v{S}t\v{e}p\'{a}n and Saboo, Ashutosh and Fernando, Isuru and Kulal, Sumith and Cimrman, Robert and Scopatz, Anthony},
     year = 2017,
     month = jan,
     keywords = {Python, Computer algebra system, Symbolics},
     abstract = {
                SymPy is an open source computer algebra system written in pure Python. It is built with a focus on extensibility and ease of use, through both interactive and programmatic applications. These characteristics have led SymPy to become a popular symbolic library for the scientific Python ecosystem. This paper presents the architecture of SymPy, a description of its features, and a discussion of select submodules. The supplementary material provide additional examples and further outline details of the architecture and features of SymPy.
             },
     volume = 3,
     pages = {e103},
     journal = {PeerJ Computer Science},
     issn = {2376-5992},
     url = {https://doi.org/10.7717/peerj-cs.103},
     doi = {10.7717/peerj-cs.103}
    }