一个可识别的成本意识的因果决策框架,使用反事实推理.
Ruichu Cai1, Xi Chen1, Jie Qiao1
1School of Computer Science, Guangdong University of Technology, Guangzhou, 510006, China.
概括
本研究引入了对异常条件的最低成本因果决策 (MiCCD) 框架,通过纳入成本和因果推理来改善决策. 与传统方法相比,MiCCD提高了系统恢复效率和成本效益.
科学领域:
- 人工智能的人工智能
- 因果推理因果推理
- 系统工程 系统工程
背景情况:
- 在异常条件下的决策对于系统恢复至关重要.
- 现有的框架往往忽视了行动成本或适当的因果机制.
- 在这些场景中,强化学习和根本原因分析存在局限性.
研究的目的:
- 提出一个最低成本因果决策 (MiCCD) 框架,使用反事实推理.
- 解决与混合异常数据识别反事实推理的挑战.
- 在持续的决策空间中优化干预策略,同时考虑成本.
主要方法:
- 基于带有异常模式集群标签的因果图表开发了一个代孕模型.
- 接近了可识别的反事实推理的结构因果模型.
- 采用了顺序最小平方编程 (SLSQP) 算法来实现成本意识的优化.
主要成果:
- 在F1得分,成本效益和排名质量 (nDCG@k) 方面,MiCCD表现出卓越的表现.
- 在合成和现实世界数据集上验证的有效性.
- 在异常状况管理方面表现优于传统的决策方法.
结论:
- 该MiCCD框架有效地整合了因果推断和成本优化,用于异常条件决策.
- 对于复杂的系统管理,MiCCD提供了一个强大的,广泛适用的解决方案.
- 这种方法提高了恢复系统正常状态的效率和成本效益.
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