Python 3 implementation of the methods presented in:
Efficient WMI via SMT-Based Predicate Abstraction
Paolo Morettin, Andrea Passerini, Roberto Sebastiani,
in Proceedings of IJCAI 2017
Advanced smt techniques for Weighted Model Integration
Paolo Morettin, Andrea Passerini, Roberto Sebastiani,
in Artificial Intelligence, Volume 275, 2019
SMT-based Weighted Model Integration with Structure Awareness
Giuseppe Spallitta, Gabriele Masina, Paolo Morettin, Andrea Passerini, Roberto Sebastiani,
in UAI Conference 2022
Enhancing SMT-based Weighted Model Integration by structure awareness
Giuseppe Spallitta, Gabriele Masina, Paolo Morettin, Andrea Passerini, Roberto Sebastiani,
in Artificial Intelligence, Volume 328, 2024
Base version:
pip install wmipa
The base version of the solver requires at least one integration backend to be installed.
Additional dependencies are needed to also support NRA theory.
We provide a script that automatically installs all the requirements. The script has only been tested on Ubuntu distributions.
To install all the mandatory and optional requirements, run
wmipa-install --all
and then add the following lines to the ~/.bashrc
file:
PATH=$HOME/.wmipa/latte/bin:$PATH
PATH=$HOME/.wmipa/approximate-integration/bin:$PATH
If you want to install the requirements separately, you can use the following commands.
At least one following integration backend is needed:
-
LattE integrale - Exact integration (recommended):
wmipa-install --latte
Add
$HOME/latte/bin
to the PATH environment variable by adding the following line to the~/.bashrc
file:PATH=$HOME/.wmipa/latte/bin:$PATH
-
VolEsti - Approximated integration:
wmipa-install --volesti
Add
bin
to the PATH environment variable by adding the following line to the~/.bashrc
file:PATH=$HOME/.wmipa/approximate-integration/bin:$PATH
-
PyXadd - Symbolic integration:
wmipa-install --symbolic
The MathSAT5 SMT solver is required
wmipa-install --msat
To support NRA theory (PI, Sin, Exp, ecc.), a customized version of PySMT must be installed via
wmipa-install --nra
We provide some examples that show how to write a model and evaluate weighted model integrals on it. To run the code in examples/, type:
python exampleX.py
The code for running the experiments reported in the papers above can be found in the experiments
branch.