Bayesian Case-Exclusion and Personalized Explanations for Sustainable Dairy Farming (Extended Abstract)
Abstract
Smart agriculture (SmartAg) has emerged as a rich domain for AI-driven decision support systems (DSS); however, it is often challenged by user-adoption issues. This paper reports a case-based reasoning (CBR) system, PBI-CBR, that predicts grass growth for dairy farmers, that combines predictive accuracy and explanations to improve user adoption. PBI-CBR’s key novelty is its use of Bayesian methods for case-base maintenance in a regression domain. Experiments report the tradeoff between predictive accuracy and explanatory capability for different variants of PBI-CBR, and how updating Bayesian priors each year improves performance.
Cite
Text
Kenny et al. "Bayesian Case-Exclusion and Personalized Explanations for Sustainable Dairy Farming (Extended Abstract)." International Joint Conference on Artificial Intelligence, 2020. doi:10.24963/IJCAI.2020/657Markdown
[Kenny et al. "Bayesian Case-Exclusion and Personalized Explanations for Sustainable Dairy Farming (Extended Abstract)." International Joint Conference on Artificial Intelligence, 2020.](https://mlanthology.org/ijcai/2020/kenny2020ijcai-bayesian/) doi:10.24963/IJCAI.2020/657BibTeX
@inproceedings{kenny2020ijcai-bayesian,
title = {{Bayesian Case-Exclusion and Personalized Explanations for Sustainable Dairy Farming (Extended Abstract)}},
author = {Kenny, Eoin M. and Ruelle, Elodie and Geoghegan, Anne and Shalloo, Laurence and O'Leary, Micheál and O'Donovan, Michael and Temraz, Mohammed and Keane, Mark T.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2020},
pages = {4740-4744},
doi = {10.24963/IJCAI.2020/657},
url = {https://mlanthology.org/ijcai/2020/kenny2020ijcai-bayesian/}
}