@inproceedings{Cardoso23a,
author = {Al Shukairi, Hilal and Cardoso, Rafael C.},
title = {ML-MAS: A Hybrid AI Framework for Self-Driving Vehicles},
year = {2023},
isbn = {9781450394321},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
address = {Richland, SC},
abstract = {Machine Learning (ML) techniques have been shown to be widely successful in environments that require processing a large amount of perception data, such as in fully autonomous self-driving vehicles. Nevertheless, in such a complex domain, ML-only approaches have several limitations. In this paper, we propose a hybrid Artificial Intelligence (AI) framework for fully autonomous self-driving vehicles that uses rule-based agents from symbolic AI to supplement the ML models in their decision-making. Our framework is evaluated using routes from the CARLA simulation environment, and has been shown to improve the driving score of the ML models.},
booktitle = {Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems},
pages = {1191–1199},
numpages = {9},
url          = {https://dl.acm.org/doi/10.5555/3545946.3598762},
doi          = {10.5555/3545946.3598762},
keywords = {hybrid AI, BDI agents, CARLA, self-driving vehicles, deep learning},
location = {London, United Kingdom},
series = {AAMAS '23}
}

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