通过神经编码来阐明线性程序
Florian Peter Busch1,2, Matej Zečević1, Kristian Kersting1,2,3,4
1Department of Computer Science, Technical University of Darmstadt, Darmstadt, Germany.
Frontiers in artificial intelligence
|July 3, 2025
概括
从线性程序 (LPs) 中解释解决方案是具有挑战性的. 这项研究表明,将LP编码为神经网络如何实现有效的解释方法,提高AI的解释性.
科学领域:
- 人工智能的人工智能
- 运营研究 运营研究
- 机器学习 机器学习
背景情况:
- 线性程序 (LPs) 是人工智能和优化的基础.
- 现有的可解释AI (XAI) 方法主要关注深度学习,忽视LP.
- 尽管LP是白盒,但在理解输入-输出关系方面存在挑战.
研究的目的:
- 开发方法来解释线性程序的解决方案.
- 调整现有的归因方法来解释LP输出.
- 提高利用LP的AI系统的可解释性.
主要方法:
- 将线性程序编码为神经网络格式.
- 对神经LP编码进行Saliency和LIME等归因方法的调整.
- 评估各种LP上的解释方法,包括大规模实例 (10k维度).
主要成果:
- 神经编码成功地使得可以将归因方法应用于LP.
- 提出的方法证明了LP解决方案的可解释性.
- 度和LIME在低扰动级别下表现相似.
结论:
- 线性程序可以并且应该被解释为更好的AI透明度.
- 将LP表示为神经网络是一种可行的策略,可以提高其可解释性.
- 这项工作弥合了优化和可解释的人工智能之间的差距.
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