Decision-Making Under Uncertainty in Multi-Agent and Multi-Robot Systems: Planning and Learning
Abstract
Multi-agent planning and learning methods are becoming increasingly important in today's interconnected world. Methods for real-world domains, such as robotics, must consider uncertainty and limited communication in order to generate high-quality, robust solutions. This paper discusses our work on developing principled models to represent these problems and planning and learning methods that can scale to realistic multi-agent and multi-robot tasks.
Cite
Text
Amato. "Decision-Making Under Uncertainty in Multi-Agent and Multi-Robot Systems: Planning and Learning." International Joint Conference on Artificial Intelligence, 2018. doi:10.24963/IJCAI.2018/805Markdown
[Amato. "Decision-Making Under Uncertainty in Multi-Agent and Multi-Robot Systems: Planning and Learning." International Joint Conference on Artificial Intelligence, 2018.](https://mlanthology.org/ijcai/2018/amato2018ijcai-decision/) doi:10.24963/IJCAI.2018/805BibTeX
@inproceedings{amato2018ijcai-decision,
title = {{Decision-Making Under Uncertainty in Multi-Agent and Multi-Robot Systems: Planning and Learning}},
author = {Amato, Christopher},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2018},
pages = {5662-5666},
doi = {10.24963/IJCAI.2018/805},
url = {https://mlanthology.org/ijcai/2018/amato2018ijcai-decision/}
}