DeepProbLog is an extension of ProbLog that integrates Probabilistic Logic Programming with deep learning by introducing the neural predicate. The neural predicate represents probabilistic facts whose probabilites are parameterized by neural networks. For more information, consult the papers listed below.
DeepProbLog can easily be installed using the following command: Make sure the following packages are installed:
pip install deepproblog
To make sure your installation works, install pytest
pip install pytest
and run
python -m deepproblog test
DeepProbLog has the following requirements:
- Python > 3.9
- ProbLog
- PySDD
- PyTorch
- TorchVision
To use Approximate Inference, we have the followign additional requirements
- PySwip
- Use
pip install git+https://github.com/ML-KULeuven/pyswip
- Use
- SWI-Prolog < 9.0.0 The latter can be installed on Ubuntu with the following commands:
sudo apt-add-repository ppa:swi-prolog/stable
sudo apt install swi-prolog=8.4* swi-prolog-nox=8.4* swi-prolog-x=8.4*
The experiments are presented in the papers are available in the src/deepproblog/examples directory.
- Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, Luc De Raedt: DeepProbLog: Neural Probabilistic Logic Programming. NeurIPS 2018: 3753-3763 (paper)
- Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, Luc De Raedt: Neural Probabilistic Logic Programming in DeepProbLog. AIJ (paper)
- Robin Manhaeve, Giuseppe Marra, Luc De Raedt: Approximate Inference for Neural Probabilistic Logic Programming. KR 2021
Copyright 2023 KU Leuven, DTAI Research Group
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