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.

该研究解决了提升人工智能推理能力的挑战，特别是在抽象与推理语料库（ARC）中的逻辑性问题。论文提出了一种新框架，通过将观察数据符号化为知识图谱，提取核心知识，旨在缩小解决方案搜索空间，从而改善人工智能在ARC任务中的表现，提供合理且逻辑的解决方案。

抽象与推理语料库上的溯因符号求解器