从feedforward神经网络推导出基于符号的等价决策模型
Sebastian Seidel1, Uwe M Borghoff2
1KNDS Deutschland GmbH & Co. KG, Munich, Germany.
Frontiers in artificial intelligence
|August 8, 2025
概括
本研究介绍了一种方法,从人工智能 (AI) 推进神经网络 (FNN) 中提取可解释的决策树. 这种方法通过弥合象征性和连接主义AI范式来提高AI透明度和信任.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机科学 计算机科学
背景情况:
- 人工智能 (AI) 的采用速度很快,但系统的不透明性阻碍了信任.
- 深度学习和自然语言处理推动了人工智能的发展.
- 可解释的AI对于接受和问责至关重要.
研究的目的:
- 从前神经网络 (FNN) 来导出可解释的符号模型,特别是决策树.
- 为了弥合连接主义和象征性AI方法之间的差距.
- 通过透明度加强对人工智能系统的信任和问责制.
主要方法:
- 从FNN中提取符号组件 (填充物,角色,关系) 的系统方法.
- 在网络层中追踪神经元激活值和输入配置.
- 为提高透明度,将活动和输入映射到决策树边缘.
主要成果:
- 成功导出了捕捉FNN决策流程的决策树.
- 通过代改进,向更深层的网络展示了可扩展性.
- 开发了一种验证从神经网络中提取符号表示的原型.
结论:
- 提出的方法有效地将复杂的FNN转化为可解释的决策树.
- 这种方法增强了人工智能系统的信任和问责制.
- 该方法为更透明,更易于理解的人工智能提供了一条途径.
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