在自我调节的认知架构中,局部可解释性的认识界限
1Université Frères Mentouri Constantine 1, Constantine, Algeria.
Frontiers in artificial intelligence
|December 17, 2025
概括
像LIME和SHAP这样的局部解释性方法在复杂的认知架构中失败. 需要新的基于叙事的方法来实现透明和可理解的AI系统.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
- 计算神经科学是一种神经科学.
背景情况:
- 当地解释性方法 (LIME,SHAP) 是解释AI模型的标准.
- 这些方法假设局部连续性,这在递归认知架构中经常被侵犯.
研究的目的:
- 在解释复杂的认知系统时分析局部代理模型的局限性.
- 在AI中引入新的构造,以提高可解释性.
主要方法:
- 采用了正式推理和模拟实验.
- 引入了模块化认知注意力 (MCA),认知飞跃操作员 (Ψ) 和内部叙述生成器 (ING).
主要成果:
- 局部干扰产生了取决于状态的解释结果.
- 叙事连贯性来自于递归的政策动态,而不是局部归因.
- 传统的方法无法识别决策空间的分叉点.
结论:
- 对于递归认知架构来说,临时局部近似是不够的.
- 为AGI系统提出了嵌入式基于叙事的解释性.
- 这种方法增强了认识系统的透明度,并与认知理解理论保持一致.
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