This invited review discusses causal learning in the context of robotic intelligence. The paper introduced the psychological findings on causal learning in human cognition, then it introduced the traditional statistical solutions on causal discovery and causal inference. The paper reviewed recent deep causal learning algorithms with a focus on their architectures and the benefits of using deep nets and discussed the gap between deep causal learning and the needs of robotic intelligence.
翻译:受邀审查讨论了机器人情报背景下的因果学习,文件介绍了关于人类认知中因果学习的心理结论,然后介绍了关于因果发现和因果推断的传统统计解决办法,文件审查了最近的深因果学习算法,重点是其架构和使用深网的益处,并讨论了深因果学习与机器人情报需求之间的差距。