Open-World Learning for Radically Autonomous Agents
Abstract
In this paper, I pose a new research challenge – to develop intelligent agents that exhibit radical autonomy by responding to sudden, long-term changes in their environments. I illustrate this idea with examples, identify abilities that support it, and argue that, although each ability has been studied in isolation, they have not been combined into integrated systems. In addition, I propose a framework for characterizing environments in which goal-directed physical agents operate, along with specifying the ways in which those environments can change over time. In closing, I outline some approaches to the empirical study of such open-world learning.
Cite
Text
Langley. "Open-World Learning for Radically Autonomous Agents." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I09.7078Markdown
[Langley. "Open-World Learning for Radically Autonomous Agents." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/langley2020aaai-open/) doi:10.1609/AAAI.V34I09.7078BibTeX
@inproceedings{langley2020aaai-open,
title = {{Open-World Learning for Radically Autonomous Agents}},
author = {Langley, Pat},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2020},
pages = {13539-13543},
doi = {10.1609/AAAI.V34I09.7078},
url = {https://mlanthology.org/aaai/2020/langley2020aaai-open/}
}