Learning and Planning under Uncertainty for Conservation Decisions
DOI:
https://doi.org/10.1609/aaai.v37i13.26930Keywords:
Online Learning, Reinforcement Learning, Sequential Decision-making, Conservation, EnvironmentAbstract
My research focuses on new techniques in machine learning and game theory to optimally allocate our scarce resources in multi-agent settings to maximize environmental sustainability. Drawing scientific questions from my close partnership with conservation organizations, I have advanced new lines of research in learning and planning under uncertainty, inspired by the low-data, noisy, and dynamic settings faced by rangers on the frontlines of protected areas.Downloads
Published
2024-07-15
How to Cite
Xu, L. (2024). Learning and Planning under Uncertainty for Conservation Decisions. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16139-16140. https://doi.org/10.1609/aaai.v37i13.26930
Issue
Section
AAAI Doctoral Consortium Track