Integrating Learning into a BDI Agent for Environments with Changing Dynamics
Abstract
We propose a framework that adds learning for improving plan selection in the popular BDI agent programming paradigm. In contrast with previous proposals, the approach given here is able to scale up well with the complexity of the agent's plan library. Technically, we develop a novel confidence measure which allows the agent to adjust its reliance on the learning dynamically, facilitating in principle infinitely many (re)learning phases. We demonstrate the benefits of the approach in an example controller for energy management.
Cite
Text
Singh et al. "Integrating Learning into a BDI Agent for Environments with Changing Dynamics." International Joint Conference on Artificial Intelligence, 2011. doi:10.5591/978-1-57735-516-8/IJCAI11-420Markdown
[Singh et al. "Integrating Learning into a BDI Agent for Environments with Changing Dynamics." International Joint Conference on Artificial Intelligence, 2011.](https://mlanthology.org/ijcai/2011/singh2011ijcai-integrating/) doi:10.5591/978-1-57735-516-8/IJCAI11-420BibTeX
@inproceedings{singh2011ijcai-integrating,
title = {{Integrating Learning into a BDI Agent for Environments with Changing Dynamics}},
author = {Singh, Dhirendra and Sardiña, Sebastian and Padgham, Lin and James, Geoff},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2011},
pages = {2525-2530},
doi = {10.5591/978-1-57735-516-8/IJCAI11-420},
url = {https://mlanthology.org/ijcai/2011/singh2011ijcai-integrating/}
}