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首页> 外文会议>International Conference on Case-Based Reasoning >Predicting Grass Growth for Sustainable Dairy Farming: A CBR System Using Bayesian Case-Exclusion and Post-Hoc, Personalized Explanation-by-Example (XAI)
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Predicting Grass Growth for Sustainable Dairy Farming: A CBR System Using Bayesian Case-Exclusion and Post-Hoc, Personalized Explanation-by-Example (XAI)

机译：预测奶牛可持续生长的草木生长：使用贝叶斯案例排除法和事后事后个性化示例解释（XAI）的CBR系统

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摘要

Smart agriculture has emerged as a rich application domain for Al-driven decision support systems (DSS) that support sustainable and responsible agriculture, by improving resource-utilization through better on-farm, management decisions. However, smart agriculture's promise is often challenged by the high barriers to user adoption. This paper develops a case-based reasoning (CBR) system called PBI-CBR to predict grass growth for dairy farmers, that combines predictive accuracy and explanation capabilities designed to improve user adoption. The system provides post-hoc, personalized explanation-by-example for its predictions, by using explanatory cases from the same farm or county. A key novelty of PBI-CBR is its use of Bayesian methods for case exclusion in this regression domain. Experiments report the tradeoff that occurs between predictive accuracy and explanatory adequacy for different parametric variants of PBI-CBR, and how updating Bayesian priors each year reduces error.
机译：智慧农业已经成为Al驱动的决策支持系统（DSS）的丰富应用领域，该系统通过更好的农场管理决策来改善资源利用，从而支持可持续和负责任的农业。但是，智能农业的前景常常受到用户采用的高障碍的挑战。本文开发了一种称为PBI-CBR的基于案例的推理（CBR）系统，用于预测奶农的草木生长，该系统结合了旨在提高用户采用率的预测准确性和解释能力。该系统通过使用来自同一农场或县的解释性案例，提供事后的个性化示例性预测解释。 PBI-CBR的一个关键新颖之处是在此回归域中使用贝叶斯方法进行案例排除。实验报告了在PBI-CBR的不同参数变体的预测准确性和解释性充分性之间的权衡，以及每年更新贝叶斯先验如何减少误差。

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