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 Bremen, Germany.
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.
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 1.1.2 (14.03.2017)
- Fix: Patches for using toulbar2 on Windows platforms
- Release 1.1.1 (13.03.2017)
- Fix: Patches for Windows support
- Release 1.1.0 (13.06.2016)