Bayesian Case-Exclusion and Personalized Explanations for Sustainable Dairy Farming (Extended Abstract)
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Sister Conferences Best Papers. Pages 4740-4744. https://doi.org/10.24963/ijcai.2020/657
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.
Knowledge Representation and Reasoning: Case-based Reasoning
Machine Learning: Interpretability
Machine Learning: Explainable Machine Learning
AI Ethics: Explainability