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About
=====
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.
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 :doc:`apidoc`.
Release notes
^^^^^^^^^^^^^
* Release 1.2.1 (18.12.2017)
* Support for Python 2 and Python 3
* Release a ``pip``-compliant package
* Minor fixes
* 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)
* *Fix*: :ref:`sec-cppbindings`
* *Feature*: literal groups for formula expansion (see :ref:`sec-litgroups`)
* *Fix*: existentially quantified formulas evaluate to false when they cannot be grounded
* *Fix*: cleanup of process pools in multicore mode
Contents
^^^^^^^^
.. toctree::
:maxdepth: 2
features
installation
tools
mlnquerytool
mlnlearningtool
learningmethods
inferencemethods
mln_syntax
mlntutorial
evaluation
apidoc
tutorial
Credits
^^^^^^^
Lead Developer
~~~~~~~~~~~~~~
Daniel Nyga (`nyga@cs.uni-bremen.de `_)
Contributors
~~~~~~~~~~~~
* 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
Acknowledgments
~~~~~~~~~~~~~~~
This work is supported in part by the EU FP7 projects `RoboHow `_ (grant number 288533) and `ACAT `_ (grant number
600578):
.. image:: _static/img/robohow-logo.png
:height: 70px
:target: http://www.robohow.eu
.. image:: _static/img/acat-logo.png
:height: 70px
:target: http://www.acat-project.eu
.. image:: _static/img/fp7-logo.png
:height: 70px
:target: http://ec.europa.eu/research/fp7/index_en.cfm
Publications
^^^^^^^^^^^^
.. bibliography:: refs.bib
:list: enumerated
:enumtype: arabic
:filter: author % "Nyga" or author % "Jain"
:all:
Indices and tables
^^^^^^^^^^^^^^^^^^
* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`