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

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Author(s)
Kenny, Eoin M. 
Ruelle, Elodie 
Geoghegan, Anne 
Temraz, Mohammed 
Keane, Mark T. 
et al. 
Uri
http://hdl.handle.net/10197/12206
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
Keywords
  • Recommender systems

  • Agriculture

  • Sustainability

  • Artificial intelligen...

Web versions
https://ijcai20.org/
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
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Owning collection
Insight Research Collection
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Acquisition Date
Jan 27, 2023
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Downloads
115
Last Week
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Acquisition Date
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