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基于人类模型交互的决策支持系统,用于优化食品安全评估
Canghong Jin1, Yuanhong Xiao2, Hao Wu3
1Hangzhou City University, Huzhou Street 51, Hangzhou 310015, Zhejiang Province, China.
Food research international (Ottawa, Ont.)
|April 23, 2025
概括
本研究介绍了模型-人与人交互风险评估 (MHRA) 框架,增强决策支持系统 (DSS) 的人类投入,以提高数据质量和模型优化. 它通过将专家知识整合到循环中来提高准确性和食品安全.
科学领域:
- 决策支持系统是什么?
- 机器学习 机器学习
- 食品安全 食品安全
背景情况:
- 现实世界决策支持系统 (DSS) 需要高质量的数据来持续运行,但数据采集通常是昂贵的和资源密集的,特别是在专业领域.
- 机器学习研究越来越多地探索整合实验数据和专家知识,以克服数据采集的挑战.
- "循环中的人类" (HITL) 方法正在获得吸引力,因为它能够确保专家系统的知识库保持最新.
研究的目的:
- 介绍一个新的框架,模型-人与人互动风险评估 (MHRA),旨在通过人与人互动和协作场景构建来提高DSS性能.
- 通过整合各个DSS阶段的人类投入来解决更新的专家系统知识库的需求.
- 展示人类互动模拟模型如何提高决策准确性和标准化,同时最大限度地降低食品安全风险.
主要方法:
- 模型-人与人互动风险评估 (MHRA) 框架的开发.
- 将人类互动和协作场景构建集成到DSS工作流程中.
- 应用人类互动模拟模型来协助决策者.
主要成果:
- MHRA框架利用人类互动来提高DSS性能和数据质量.
- "循环中的人类" (HITL) 方法确保了知识库的持续更新.
- 人类互动模拟模型有助于最大限度地提高评估模型的准确性和标准化.
结论:
- MHRA框架提供了一种实际的解决方案,通过将人类专业知识纳入来改善决策支持系统.
- 该研究强调了人与人互动模拟在尽量减少食品安全风险方面的有效性.
- 婴儿食品评估案例研究证明了该框架的适用性,并提供了对其优势和局限性的见解.
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