pracmln is a toolbox for statistical relational learning and
reasoning and as such also includes tools for standard graphical
models. pracmln is a statistical relational learning and reasoning
system that supports efficient learning and inference in relational
domains. pracmln has started as a fork of the ProbCog toolbox and
has been extended by latest developments in learning and reasoning
by the Institute for Artificial Intelligence at the University of
pracmln was designed with the particular needs of technical systems
in mind. Our methods are geared towards practical applicability and
can easily be integrated into other applications. The tools for
relational data collection and transformation facilitate
data-driven knowledge engineering, and the availability of
graphical tools makes both learning or inference sessions a
user-friendly experience. Scripting support enables automation, and
for easy integration into robotics applications, we provide a
client-server library implemented using the widely used ROS (Robot
Operating System) middleware.
- Markov logic networks (MLNs): learning and inference Fuzzy-MLN
reasoning, probabilistic reasoning about concept taxonomies.
- Logic: representation, propositionalization,
stochastic SAT sampling, weighted SAT solving, etc.
This package consists of an implementation of Markov logic networks
as a Python module (pracmln) that you can use to work with MLNs in
your own Python scripts. For an introduction into using pracmln in
your own scripts, see API-Specification.
- Release 1.1.0 (13.06.2016)
- Fix: C++ bindings
- Feature: literal groups for formula expansion (see Grouping Literals)
- Fix: existentially quantified formulas evaluate to false when they cannot be grounded
- Fix: cleanup of process pools in multicore mode
- Mareike Picklum
- Ferenc Balint-Benczedi
- Thiemo Wiedemeyer
- Valentine Chiwome
Former Contributors (from ProbCog)
- Dominik Jain
- Stefan Waldherr
- Klaus von Gleissenthall
- Andreas Barthels
- Ralf Wernicke
- Gregor Wylezich
- Martin Schuster
- Philipp Meyer
- D Allouche, S de Givry, and T Schiex. Toulbar2, an open source exact cost function network solver. Technical Report, Technical report, INRIA, 2010.
- Dominik Jain. Knowledge Engineering with Markov Logic Networks: A Review. In DKB 2011: Proceedings of the Third Workshop on Dynamics of Knowledge and Belief. 2011.
- Dominik Jain and Michael Beetz. Soft Evidential Update via Markov Chain Monte Carlo Inference. In KI 2010: Advances in Artificial Intelligence, 33rd Annual German Conference on AI, volume 6359 of Lecture Notes in Computer Science, 280–290. Springer, 2010.
- Dominik Jain, Paul Maier, and Gregor Wylezich. Markov Logic as a Modelling Language for Weighted Constraint Satisfaction Problems. In Eighth International Workshop on Constraint Modelling and Reformulation, in conjunction with CP2009. 2009.
- Gheorghe Lisca, Daniel Nyga, Ferenc Bálint-Benczédi, Hagen Langer, and Michael Beetz. Towards Robots Conducting Chemical Experiments. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Hamburg, Germany, 2015.
- Daniel Nyga, Ferenc Balint-Benczedi, and Michael Beetz. PR2 Looking at Things: Ensemble Learning for Unstructured Information Processing with Markov Logic Networks. In IEEE International Conference on Robotics and Automation (ICRA). Hong Kong, China, May 31-June 7 2014.
- Daniel Nyga and Michael Beetz. Cloud-based Probabilistic Knowledge Services for Instruction Interpretation. In International Symposium of Robotics Research (ISRR). Sestri Levante (Genoa), Italy, 2015.
- Daniel Nyga and Michael Beetz. Reasoning about Unmodelled Concepts – Incorporating Class Taxonomies in Probabilistic Relational Models. In Arxiv.org. 2015. Preprint: http://arxiv.org/abs/1504.05411.