一项关于推推因果推理的调查
Huishi Luo1, Fuzhen Zhuang1,2, Ruobing Xie3
1Institute of Artificial Intelligence, Beihang University, Beijing 100191, China.
Innovation (Cambridge (Mass.))
|March 1, 2024
概括
这项调查系统地审查了推系统 (RS) 中的因果推断. 它提出了一个基于因果理论的新分类学,帮助研究人员理解和应用这些方法.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 在推系统 (RS) 中,因果推理对于理解因果关系越来越重要.
- 现有的调查经常根据实际问题对方法进行分类,使统一的因果理论碎片化.
- RS研究人员需要对因果推理有一个连贯的,理论驱动的观点.
研究的目的:
- 从因果理论的角度系统地审查RS的因果推理.
- 提出一种新的分类法来对RS中现有的因果推理方法进行分类.
- 为了促进在RS领域内更深入地整合因果推理.
主要方法:
- 介绍了因果推理的基本概念.
- 提出了理论驱动的分类学:潜在的结果框架,结构性因果模型和一般的反事实.
- 审查了应用因果推理到RS问题的技术细节.
主要成果:
- 提供了RS文献中因果推理的系统审查.
- 将现有方法组织成一个基于潜在因果理论的新型分类学.
- 确定因果推系统的关键挑战和未来研究方向.
结论:
- 以理论为导向的方法对于推进RS的因果推理至关重要.
- 拟议的分类学为不同的因果关系方法提供了一个统一的视角.
- 需要进一步的研究,以充分利用因果推理来改进推者系统.
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