论文标题
从书籍或视频/电影库中提取因果关系
Causal DAG extraction from a library of books or videos/movies
论文作者
论文摘要
确定正在考虑的问题的因果DAG（定向无环图）是在统计中进行Judea Pearl的因果推理（CI）时的主要障碍。在人工智能（AI）和机器学习（ML）中进行CI时，出现了同样的问题。与科学领域的许多问题一样，我们认为大自然已经找到了解决这个问题的有效解决方案。我们认为，人类和动物的大脑包含用于执行CI的明确引擎，并且这种引擎使用了因果关系的输入ATLA（即收集）。我们提出了一种简单的算法，用于从书籍或视频/电影库中构建此类地图集。我们通过将其应用于随机生成的TIC-TAC-TOE游戏的数据库来说明我们的方法。用于生成此TIC-TAC示例的软件是开源的，可在GitHub上找到。
Determining a causal DAG (directed acyclic graph) for a problem under consideration, is a major roadblock when doing Judea Pearl's Causal Inference (CI) in Statistics. The same problem arises when doing CI in Artificial Intelligence (AI) and Machine Learning (ML). As with many problems in Science, we think Nature has found an effective solution to this problem. We argue that human and animal brains contain an explicit engine for doing CI, and that such an engine uses as input an atlas (i.e., collection) of causal DAGs. We propose a simple algorithm for constructing such an atlas from a library of books or videos/movies. We illustrate our method by applying it to a database of randomly generated Tic-Tac-Toe games. The software used to generate this Tic-Tac-Toe example is open source and available at GitHub.
