Learning and Planning under Uncertainty for Conservation Decisions

Authors

  • Lily Xu Harvard University

DOI:

https://doi.org/10.1609/aaai.v37i13.26930

Keywords:

Online Learning, Reinforcement Learning, Sequential Decision-making, Conservation, Environment

Abstract

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.

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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