Co-Optimizating Multi-Agent Placement with Task Assignment and Scheduling
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. PDF
Cite
Text
Zhang and Shah. "Co-Optimizating Multi-Agent Placement with Task Assignment and Scheduling." International Joint Conference on Artificial Intelligence, 2016.Markdown
[Zhang and Shah. "Co-Optimizating Multi-Agent Placement with Task Assignment and Scheduling." International Joint Conference on Artificial Intelligence, 2016.](https://mlanthology.org/ijcai/2016/zhang2016ijcai-co/)BibTeX
@inproceedings{zhang2016ijcai-co,
title = {{Co-Optimizating Multi-Agent Placement with Task Assignment and Scheduling}},
author = {Zhang, Chongjie and Shah, Julie A.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2016},
pages = {3308-3314},
url = {https://mlanthology.org/ijcai/2016/zhang2016ijcai-co/}
}