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Behavior Modification01:21

Behavior Modification

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Behavioral approaches have often been criticized for ignoring mental processes and focusing solely on observable behavior. However, these approaches provide an optimistic perspective for individuals seeking to change their behaviors. Rather than concentrating on intrinsic personality traits, behavioral approaches suggest that even longstanding habits can be modified by changing the reward contingencies that maintain them.
A real-world application of operant conditioning principles is applied...
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关于评估黑盒可解释的人工智能方法,以提高自动驾驶系统中异常检测的性能

Sazid Nazat1, Osvaldo Arreche1, Mustafa Abdallah2

  • 1Electrical and Computer Engineering Department, Purdue School of Engineering and Technology, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA.

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概括
此摘要是机器生成的。

本研究引入了一个框架,用于评估可解释的AI (XAI) 技术,用于检测自动驾驶汽车 (AV) 中的网络安全异常. 它评估了SHAP和LIME方法,为安全的AV网络开发提供了洞察力.

关键词:
在 LIME 时代,沙普利添加剂的解释维雷米数据集是VeReMi的数据集.检测异常检测异常检测自动驾驶自动驾驶的自动驾驶.可以解释的人工智能AI特性提取 特性提取

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科学领域:

  • 网络安全 网络安全
  • 人工智能的人工智能
  • 自主系统 自主系统

背景情况:

  • 自动驾驶汽车 (AV) 面临着针对其网络的网络安全风险.
  • 人工智能模型对于检测AV中网络异常至关重要.
  • 可解释性AI (XAI) 对于理解AI异常检测决策至关重要.

研究的目的:

  • 引入一个全面的框架来评估黑盒XAI技术在AV异常检测.
  • 评估全球和本地XAI方法在解释AI行为的有效性.
  • 为AV网络安全提供XAI的优势和局限性的洞察力.

主要方法:

  • 开发了一个评估XAI技术的框架,使用六个指标:描述性准确性,稀疏性,稳定性,效率,稳定性和完整性.
  • 评估了两个黑盒XAI技术:SHAP (夏普利添加式解释) 和LIME (局部可解释模型不可知解释).
  • 应用了XAI技术来识别VeReMi和传感器数据集上的异常分类的关键特征.

主要成果:

  • 使用两个AV数据集对六个指标进行了SHAP和LIME的评估.
  • 确定了用于使用XAI对异常AV行为进行分类至关重要的主要特征.
  • 在AV异常检测的背景下对SHAP和LIME性能进行了比较分析.

结论:

  • 该研究推进了黑盒XAI的部署,用于在自动驾驶中检测现实世界的异常.
  • 提供了对关键的AV领域当前XAI方法的能力和限制的宝贵见解.
  • 为自动驾驶汽车网络提供更强大,更透明的网络安全解决方案.