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Bayesian Case-Exclusion and Personalized Explanations for Sustainable Dairy Farming (Extended Abstract)
2020
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
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
doi:10.24963/ijcai.2020/649
dblp:conf/ijcai/ChenSHLML20
fatcat:cjn73bwpqzdcheoctkkl62fokq