arXiv 1302.15632013-02-06cs.AI

因果知识的认知加工

The Cognitive Processing of Causal Knowledge

Illinois Institute of Technology（伊利诺伊理工学院）；Northeastern Illinois University（东北伊利诺伊大学）

Scott B. Morris, Doug Cork, Richard E. Neapolitan

备注：: Appears in Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI1997)

摘要：

简要描述了使用贝叶斯网络表示、推理和学习因果结构的概率因果图模型。然后，有人认为，这个模型是密切相关的人类如何推理和学习因果结构。它表明，心理学研究折扣（推理关于如何存在的一个原因的影响，使另一个原因不太可能）支持的假设，即人类达到相同的判断算法在贝叶斯网络中进行推理。接下来，皮亚杰的研究表明，人类通过观察与某些学习贝叶斯网络结构的算法所使用的相同的独立性和依赖性来学习因果结构。基于这一指示，因果关系的主观定义。最后，进一步测试这些索赔的准确性的方法进行了讨论。

英文摘要：

There is a brief description of the probabilistic causal graph model for representing, reasoning with, and learning causal structure using Bayesian networks. It is then argued that this model is closely related to how humans reason with and learn causal structure. It is shown that studies in psychology on discounting (reasoning concerning how the presence of one cause of an effect makes another cause less probable) support the hypothesis that humans reach the same judgments as algorithms for doing inference in Bayesian networks. Next, it is shown how studies by Piaget indicate that humans learn causal structure by observing the same independencies and dependencies as those used by certain algorithms for learning the structure of a Bayesian network. Based on this indication, a subjective definition of causality is forwarded. Finally, methods for further testing the accuracy of these claims are discussed.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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