This paper addresses the challenge of enhancing artificial intelligence reasoning capabilities, focusing on logicality within the Abstraction and Reasoning Corpus (ARC). Humans solve such visual reasoning tasks based on their observations and hypotheses, and they can explain their solutions with a proper reason. However, many previous approaches focused only on the grid transition and it is not enough for AI to provide reasonable and human-like solutions. By considering the human process of solving visual reasoning tasks, we have concluded that the thinking process is likely the abductive reasoning process. Thus, we propose a novel framework that symbolically represents the observed data into a knowledge graph and extracts core knowledge that can be used for solution generation. This information limits the solution search space and helps provide a reasonable mid-process. Our approach holds promise for improving AI performance on ARC tasks by effectively narrowing the solution space and providing logical solutions grounded in core knowledge extraction.

翻译：本文旨在应对提升人工智能推理能力的挑战，重点关注抽象与推理语料库（ARC）中的逻辑性问题。人类解决此类视觉推理任务时，会基于其观察与假设，并能用恰当的理由解释其解决方案。然而，许多先前的方法仅关注网格转换，这不足以使人工智能提供合理且类人的解决方案。通过思考人类解决视觉推理任务的过程，我们推断其思维过程很可能属于溯因推理。因此，我们提出了一种新颖的框架，将观测数据符号化表示为知识图谱，并提取可用于生成解决方案的核心知识。这些信息限定了解决方案的搜索空间，并有助于提供合理的中间过程。我们的方法通过有效缩小解空间并提供基于核心知识提取的逻辑解，有望提升人工智能在ARC任务上的表现。