用符号知识图谱模拟人类推理解题，让AI推理更像人。
Abductive Symbolic Solver on Abstraction and Reasoning Corpus
- 将视觉任务转为符号知识图谱，提取核心逻辑
- 在ARC数据集上提升推理准确率，减少无效搜索
- 适合研究可解释AI和认知推理的学者
本文针对人工智能在抽象与推理语料库（ARC）中推理能力的提升问题，关注逻辑性。人类解决此类视觉推理任务依赖观察与假设，并能以合理理由解释解法。但以往方法仅关注网格变换，难以生成类人推理过程。我们发现人类解题本质是溯因推理，因此提出新框架：将观测数据符号化为知识图谱，提取核心知识用于解法生成。该机制缩小解空间，支持合理中间步骤。实验表明，该方法通过核心知识提取有效提升模型在ARC任务上的表现，推动实现更符合人类逻辑的推理。
原文摘要 · Abstract (English)
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.
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