Sparkle is a Programming by Optimisation (PbO)-based problem-solving platform designed to enable the widespread and effective use of PbO techniques for improving the state-of-the-art in solving a broad range of prominent AI problems, including SAT and AI Planning.
Specifically, Sparkle facilitates the use of:
- Automated algorithm configuration
- Automated algorithm selection
The installation process use the conda package manager (to install https://docs.conda.io/en/latest/miniconda.html`).
To get a copy of Sparkle you can clone the repository using git.
$ git clone https://github.com/ADA-research/SparkleSparkle depends on Python 3.9+, swig 3.0, gnuplot, LaTeX, and multiple Python packages. LaTeX is used to create the reports, if you want to use this functionality you will need to install it manually.
The rest of the dependencies can installed and activated with
$ conda env create -f environment.yml
$ conda activate sparkleFor detailed installation instructions see the documentation: https://sparkle-ai.readthedocs.io/
See the Examples directory for some examples on how to use Sparkle. All commands need to be executed from the root directory.
The documentation can be read at https://sparkle-ai.readthedocs.io/.
A PDF is also available in the repository at Documentation/sparkle-userguide.pdf.
Sparkle is distributed under the MIT licence
Sparkle is distributed with a number of external components, solvers, and instance sets. Descriptions and licensing information for each these are included in the Components/ and Examples/Resources/ directories.
The SATzilla 2012 feature extractor is used from http://www.cs.ubc.ca/labs/beta/Projects/SATzilla/ with some modifications. The main modification of this component is to disable calling the SAT instance preprocessor called SatELite. It is located in: Examples/Resources/Extractors/SAT-features-competition2012_revised_without_SatELite_sparkle/
If you use Sparkle for one of your papers and want to cite it, please cite our paper describing Sparkle: K. van der Blom, H. H. Hoos, C. Luo and J. G. Rook, Sparkle: Toward Accessible Meta-Algorithmics for Improving the State of the Art in Solving Challenging Problems, in IEEE Transactions on Evolutionary Computation, vol. 26, no. 6, pp. 1351-1364, Dec. 2022, doi: 10.1109/TEVC.2022.3215013.
@article{BloEtAl22,
title={Sparkle: Toward Accessible Meta-Algorithmics for Improving the State of the Art in Solving Challenging Problems},
author={van der Blom, Koen and Hoos, Holger H. and Luo, Chuan and Rook, Jeroen G.},
journal={IEEE Transactions on Evolutionary Computation},
year={2022},
volume={26},
number={6},
pages={1351--1364},
doi={10.1109/TEVC.2022.3215013}
}
Thijs Snelleman, Jeroen Rook, Holger H. Hoos, Noah Peil, Brian Schiller
Chuan Luo, Richard Middelkoop, Jérémie Gobeil, Sam Vermeulen, Marcel Baumann, Jakob Bossek, Tarek Junied, Yingliu Lu, Malte Schwerin, Marie Anastacio, Koen van der Blom
