Tools for Statistical Relational Learning
pracmln — Markov Logic Networks in Python
|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.
- 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.
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