## Introduction.

Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities.

ProbLog is a tool that allows you 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 engine tackles several tasks such as computing the marginals given evidence and learning from (partial) interpretations. ProbLog is a suite of efficient algorithms for various inference tasks. It is based on a conversion of the program and the queries and evidence to a weighted Boolean formula. This allows us to reduce the inference tasks to well-studied tasks such as weighted model counting, which can be solved using state-of-the-art methods known from the graphical model and knowledge compilation literature.

## The Language. Probabilistic Logic Programming.

ProbLog makes it easy to express complex, probabilistic models.

```0.3::stress(X) :- person(X).
0.2::influences(X,Y) :- person(X), person(Y).

smokes(X) :- stress(X).
smokes(X) :- friend(X,Y), influences(Y,X), smokes(Y).

0.4::asthma(X) <- smokes(X).

person(angelika).
person(joris).
person(jonas).
person(dimitar).

friend(joris,jonas).
friend(joris,angelika).
friend(joris,dimitar).
friend(angelika,jonas).```

Get to know the language using our interactive tutorial, check out our publications or try the above example directly in our interactive interface.

For an introduction, please consult the following papers

• Inference and learning in probabilistic logic programs using weighted Boolean formulas, Fierens, Daan, Guy Van den Broeck, Joris Renkens, Dimitar Shterionov, Bernd Gutmann, Ingo Thon, Gerda Janssens, and Luc De Raedt. arXiv preprint arXiv:1304.6810 (to appear in Theory and Practice of Logic Programming), 2013. PDF
• ProbLog: A probabilistic Prolog and its application in link discovery, L. De Raedt, A. Kimmig, and H. Toivonen, Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI-07), Hyderabad, India, pages 2462-2467, 2007. PDF

## The Engine. Powerful inference.

ProbLog2 is our 2nd generation engine to reason with the ProbLog language. The current engine builds on logic programming, knowledge compilation, the distribution semantics and probabilistic, graphical models. It allows you to:

• Compute marginal probabilities of any number of ground atoms in the presence of evidence.
• Learn the parameters of the ProbLog program from partial interpretations.

• Inference in probabilistic logic programs using weighted CNF's, D. Fierens, G. Van den Broeck, I. Thon, B. Gutmann, and L. De Raedt, Proceedings of the Twenty-Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-11), pages 211-220. AUAI Press, 2011. PDF accompanying Tech Report

• Learning the parameters of probabilistic logic programs from interpretations, B. Gutmann, I. Thon, and L. De Raedt, Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD-11), volume 6911 of LNCS (Lecture Notes in Computer Science), pages 581-596. Springer Berlin/Heidelberg, 2011. PDF accompanying Tech Report

```evidence(smokes(angelika),false).
evidence(influences(jonas,joris),false).

query(smokes(joris)).```

## Online interface. No hassle.

You can try out ProbLog using our online interface.

The previous version of ProbLog (ProbLog1) is available as part of YAP Prolog or can be downloaded from the old Problog1 webpages

## Need help? Fire any question or report a bug to our team.

#### Mailing list

There is a general ProbLog mailing list which you can use for all your ProbLog1 and ProbLog2 questions or bug reports. You can also consult the mailing list archive to see if your question has been asked before.

## The ProbLog team.

ProbLog2 was developed in the DTAI group of KULeuven, by the following people (in alphabetical order).

Other people who contributed to ProbLog1 and 2 (in alphabetical order):

Our colleagues from the probabilistic programming community.