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.
ProbLog is a Python package and can be embedded in Python or Java. Its knowledge base can be represented as Prolog/Datalog facts, CSV-files, SQLite database tables, through functions implemented in the host environment or combinations hereof.
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).
For an introduction, please consult the following papers
ProbLog2 is our second 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:
You can try out ProbLog using our online editor.
ProbLog2 is available from the following sources:
pip install problog
Extended documentation is available on ReadTheDocs.
There is also an experimental variant of ProbLog that supports continuous variables: Distributional Clauses.
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.
Other people who contributed to ProbLog1 and 2 (in alphabetical order):
Our colleagues from the probabilistic programming community.