Co-Optimizating Multi-Agent Placement with Task Assignment and Scheduling
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 3308-3313.
Abstract
To enable large-scale multi-agent coordination under temporal and spatial constraints, we formulate it as a multi-level optimization problem and develop a multi-abstraction search approach for co-optimizing agent placement with task assignment and scheduling. This approach begins with a highly abstract agent placement problem and the rapid computation of an initial solution, which is then improved upon using a hill climbing algorithm for a less abstract problem; finally, the solution is fine-tuned within the original problem space. Empirical results demonstrate that this multi-abstraction approach significantly outperforms a conventional hill climbing algorithm and an approximate mixed-integer linear programming approach.
@inproceedings{ijcai2016p468,
title = {Co-Optimizating Multi-Agent Placement with Task Assignment and Scheduling},
author = {Zhang, Chongjie and Shah, Julie A.},
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 = {3308--3313},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/468},
}
TY - CPAPER TI - Co-Optimizating Multi-Agent Placement with Task Assignment and Scheduling AU - Zhang, Chongjie AU - Shah, Julie A. 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 - 3308 EP - 3313 UR - https://ijcai.org/proceedings/2016/468 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
