莫达拉斯:面对不确定性,解决学习支持的自主系统的保证
Michael Austin Langford1, Kenneth H Chan1, Jonathon Emil Fleck1
1Department of Computer Science and Engineering, Michigan State University, 428 S Shaw Ln, East Lansing, MI 48824 USA.
概括
本研究提出了一个基于模型的框架,用于自适应的学习支持系统 (LEC). 它确保人工智能组件只在经过验证的环境中使用,从而提高关键系统的安全性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 系统安全工程系统安全工程
背景情况:
- 安全关键系统越来越多地包含启用学习的组件 (LEC).
- 在有限的环境中训练的LEC在现实世界中可能会表现出不安全的行为.
- 自动化方法对于在运行时评估和调整LEC信任至关重要.
研究的目的:
- 引入一种以模型为导向的方法来管理启用学习系统 (LES) 的自我适应.
- 为了使LES能够根据运行时环境评估LEC的可靠性.
- 确保LEC在经过验证的性能边界内运行.
主要方法:
- 在LES中开发一个运行时监控和目标模型评估框架.
- 实施自适应机制,因与可信操作环境的偏差引发自适应机制.
- 基于模型的方法来评估与功能目标对比的LEC行为.
主要成果:
- 该框架允许LES动态评估LEC是否能够在当前的运行时间环境中实现目标.
- 系统适应确保只有当LEC的学习行为值得信赖时,才能使用LEC.
- 利益相关者对LEC安全部署的信心增加.
结论:
- 拟议的框架提高了使用LECs的安全关键系统的安全性和可靠性.
- 基于目标模型评估的运行时自适应对于管理LEC不确定性是有效的.
- 利益相关者可以确信,LECs是在设计时验证的环境中使用的.
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