Bayesian Case-Exclusion and Personalized Explanations for Sustainable Dairy Farming (Extended Abstract)
Eoin M. Kenny, Elodie Ruelle, Anne Geoghegan, Laurence Shalloo, Micheál O'Leary, Michael O'Donovan, Mohammed Temraz, Mark T. Keane
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.
BibTeX
@inproceedings{ijcai2020p657,
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 = {Proceedings of the Twenty-Ninth International Joint Conference on
Artificial Intelligence, {IJCAI-20}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Christian Bessiere},
pages = {4740--4744},
year = {2020},
month = {7},
note = {Sister Conferences Best Papers},
doi = {10.24963/ijcai.2020/657},
url = {https://doi.org/10.24963/ijcai.2020/657},
}