Action Selection Methods for Multi-Agent Navigation in Crowded Environments
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 3992-3992.
Abstract
In real-time multi-agent navigation, agents need to move towards their goal positions while adapting their paths to avoid potential collisions with other agents and static obstacles. Existing methods compute motions that are optimal locally but do not account for the motions of the other agents, producing inefficient global motions especially when many agents move in a crowded space. In my thesis work, each agent has only a limited sensing range and uses online action selection techniques to dynamically adapt its motion to the local conditions. Experimental results obtained in simulation under different conditions show that the agents reach their destinations faster and use motions that minimize their overall energy consumption.
@inproceedings{ijcai2016p570,
title = {Action Selection Methods for Multi-Agent Navigation in Crowded Environments},
author = {Godoy, Julio},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {3992--3992},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/570},
}
TY - CPAPER TI - Action Selection Methods for Multi-Agent Navigation in Crowded Environments AU - Godoy, Julio T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 3992 EP - 3992 UR - https://ijcai.org/proceedings/2016/570 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
