Learning and Planning Under Uncertainty for Conservation Decisions

AAAI 2023 pp. 16139-16140

doi:10.1609/AAAI.V37I13.26930 /aaai/2023/xu2023aaai-learning/

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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Cite

Text

Xu. "Learning and Planning Under Uncertainty for Conservation Decisions." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I13.26930

Markdown

[Xu. "Learning and Planning Under Uncertainty for Conservation Decisions." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/xu2023aaai-learning/) doi:10.1609/AAAI.V37I13.26930

BibTeX

@inproceedings{xu2023aaai-learning,
  title     = {{Learning and Planning Under Uncertainty for Conservation Decisions}},
  author    = {Xu, Lily},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2023},
  pages     = {16139-16140},
  doi       = {10.1609/AAAI.V37I13.26930},
  url       = {https://mlanthology.org/aaai/2023/xu2023aaai-learning/}
}