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Bayesian Case-Exclusion and Explainable AI (XAI) for Sustainable Farming
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File | Description | Size | Format | |
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insight_publication.pdf | 856.23 KB |
Date Issued
15 January 2021
Date Available
26T11:01:59Z May 2021
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 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.
Sponsorship
Science Foundation Ireland
Other Sponsorship
Insight Research Centre
Type of Material
Conference Publication
Web versions
Language
English
Status of Item
Peer reviewed
Description
The 29th International Joint Conference on Artificial Intelligence - 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI-20), Yokohama, Japan, January 2021 (Conference postponed due to COVID-19 pandemic)
This item is made available under a Creative Commons License
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