We present ProbLog2, the state of the art implementation of the probabilistic programming language ProbLog. The ProbLog language allows the user to intuitively build programs that do not only encode complex interactions between a large sets of heterogenous components but also the inherent uncertainties that are present in real-life situations. The system provides efficient algorithms for querying such models as well as for learning their parameters from data. It is available as an online tool on the web and for download. The offline version offers both command line access to inference and learning and a Python library for building statistical relational learning applications from the system’s components.

@inproceedings{DriesECML15,
  title={ProbLog2: Probabilistic logic programming},
  author={Dries, Anton and Kimmig, Angelika and Meert, Wannes and Renkens, Joris and Van den Broeck, Guy and Vlasselaer, Jonas and De Raedt, Luc},
  booktitle={Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Demo Track},
  pages={312--315},
  year={2015},
  url = {https://starai.cs.ucla.edu/papers/DriesECMLDemo15.pdf},
  code = "https://dtai.cs.kuleuven.be/problog/index.html",
  organization={workshop}
}