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This is the official repository for the Applied Sciences paper "Mining Road Traffic Rules with Signal Temporal Logic and Grammatical Evolution".

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pigozzif/STLRulesEvolutionaryInferenceNoClass

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Mining Road Traffic Rules with Signal Temporal Logic and Grammatical Evolution

This is the official repository for the Applied Sciences paper "Mining Road Traffic Rules with Signal Temporal Logic and Grammatical Evolution", hosting all the code necessary to replicate the experiments. This work is mostly based on Federico Pigozzi's master's thesis and was carried on at the Machine Learning Laboratory (MaLeLab) at the Department of Engineering and Architecture, University of Trieste (Italy).

Scope

By running

mkdir output
java -cp libs/JGEA.jar:libs/moonlight.jar:libs/jblas-1.2.4.jar:target/STLRulesEvolutionaryInferenceNoClass.jar it.units.malelab.learningstl.Main {args}

where {args} is a placeholder for the arguments you must provide (see below), you will launch a grammar-based evolutionary optimization of the formula structure and parameters for Signal Temporal Logic rules of a real traffic dataset. At the same time, a number of evolution metadata will be saved inside the output folder. The project has been tested with Java 14.0.2.

Warning

On more recent Linux versions, dynamic libraries for jblas may not link properly. In this case, run the following commands:

unzip dlibs.zip

to unzip a directory containing the necessary .so files, then

sudo mv dlibs/libgfortran.so.3.0.0 /usr/local/lib/
sudo mv dlibs/libquadmath.so.0.0.0 /usr/local/lib/
sudo ldconfig

to move the .so files in the appropriate place and configure them.

Structure

  • src contains all the source code for the project;
  • libs contains the .jar files for the dependencies (see below);
  • grammars contains the .bnf files with the grammars;
  • data contains a sample .csv data file with one vehicle trajectory (for space issues);
  • target contains the main .jar file;
  • dlibs.zip is an emergency kit for the aforementioned warning.

Dependencies

The project relies on:

  • JGEA, for the evolutionary optimization;
  • MoonLight, for the monitoring of signal-temporal logic formulae.

The corresponding jars have already been included in the directory libs. See pom.xml for more details on dependencies.

Usage

This is a table of possible command-line arguments:

Argument Type Optional (yes/no) Default
seed integer no -
grammar string no -
input string yes -
output string yes ./output/
threads integer yes # available cores on CPU

where {...} denotes a finite and discrete set of possible choices for the corresponding argument. The description for each argument is as follows:

  • seed: the random seed for the experiment;
  • grammar: a (relative) path to the .bnf file for the grammar;
  • input: a (relative) path to the .csv file containing the data;
  • output: a (relative) path to the directory to save output files into;
  • threads: the number of threads to perform evolution with. Defaults to the number of available cores on the current CPU. Parallelization is taken care by JGEA and implements a distributed fitness assessment.

Bibliography

Please cite as

@article{pigozzi2021mining,
  title={Mining road traffic rules with signal temporal logic and grammar-based genetic programming},
  author={Pigozzi, Federico and Medvet, Eric and Nenzi, Laura},
  journal={Applied Sciences},
  volume={11},
  number={22},
  pages={10573},
  year={2021},
  publisher={MDPI}
}

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This is the official repository for the Applied Sciences paper "Mining Road Traffic Rules with Signal Temporal Logic and Grammatical Evolution".

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