Usage of automated controllers which make decisions on an environment are widespread and are often based on black-box models. We use Knowledge Compilation theory to bring explainability to the controller's decision given the state of the system. For this, we use simulated historical state-action data as input and build a compact and structured representation which relates states with actions. We implement this method in a Traffic Light Control scenario where the controller selects the light cycle by observing the presence (or absence) of vehicles in different regions of the incoming roads.
@article{arxiv.2007.04916,
title = {Explainability of Intelligent Transportation Systems using Knowledge Compilation: a Traffic Light Controller Case},
author = {Salomón Wollenstein-Betech and Christian Muise and Christos G. Cassandras and Ioannis Ch. Paschalidis and Yasaman Khazaeni},
journal= {arXiv preprint arXiv:2007.04916},
year = {2020}
}
Comments
Proc. IEEE Int. Conf. on Intelligent Transportation Systems, Rhodes, Greece, 2020. (In Press)