Multiagent Stochastic Planning with Bayesian Policy Recognition
Abstract
When operating in stochastic, partially observable, multiagent settings, it is crucial to accurately predict the actions of other agents. In my thesis work, I propose methodologies for learning the policy of external agents from their observed behavior, in the form of finite state controllers. To perform this task, I adopt Bayesian learning algorithms based on nonparametric prior distributions, that provide the flexibility required to infer models of unknown complexity. These methods are to be embedded in decision making frameworks for autonomous planning in partially observable multiagent systems.
Cite
Text
Panella. "Multiagent Stochastic Planning with Bayesian Policy Recognition." AAAI Conference on Artificial Intelligence, 2013. doi:10.1609/AAAI.V27I1.8506Markdown
[Panella. "Multiagent Stochastic Planning with Bayesian Policy Recognition." AAAI Conference on Artificial Intelligence, 2013.](https://mlanthology.org/aaai/2013/panella2013aaai-multiagent/) doi:10.1609/AAAI.V27I1.8506BibTeX
@inproceedings{panella2013aaai-multiagent,
title = {{Multiagent Stochastic Planning with Bayesian Policy Recognition}},
author = {Panella, Alessandro},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2013},
pages = {1672-1673},
doi = {10.1609/AAAI.V27I1.8506},
url = {https://mlanthology.org/aaai/2013/panella2013aaai-multiagent/}
}