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Bayesian Case-Exclusion and Explainable AI (XAI) for Sustainable Farming
appsOther research product2020 Ireland English
Authors: Kenny, Eoin M.; Ruelle, Elodie; Geoghegan, Anne; Temraz, Mohammed; +2 Authors
Kenny, Eoin M.; Ruelle, Elodie; Geoghegan, Anne; Temraz, Mohammed; Keane, Mark T.; et al.;
handle: 10197/12206
handle: 10197/12206
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) 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. Science Foundation Ireland Insight Research Centre
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The following results are related to COVID-19. Are you interested to view more results? Visit OpenAIRE - Explore.

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Bayesian Case-Exclusion and Explainable AI (XAI) for Sustainable Farming
appsOther research product2020 Ireland English
Authors: Kenny, Eoin M.; Ruelle, Elodie; Geoghegan, Anne; Temraz, Mohammed; +2 Authors
Kenny, Eoin M.; Ruelle, Elodie; Geoghegan, Anne; Temraz, Mohammed; Keane, Mark T.; et al.;
handle: 10197/12206
handle: 10197/12206
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) 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. Science Foundation Ireland Insight Research Centre
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Bayesian Case-Exclusion and Explainable AI (XAI) for Sustainable Farming

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Bayesian Case-Exclusion and Explainable AI (XAI) for Sustainable Farming

COVID-19
Research data
Research software
Other research products
2014-2023
IE
COVID-19
Energy Research
Rural Digital Europe