The Abstract Reasoning Corpus (ARC) is an intelligence tests for measuring fluid intelligence in artificial intelligence systems and humans alike. In this paper we present a system for reasoning about and solving ARC tasks. Our system relies on a program synthesis approach that searches a space of potential programs for ones that can solve tasks from the ARC. Programs are in a domain specific language, and in some instances our search algorithm is guided by insights from a corpus of ground truth programs. In particular: We describe an imperative style domain specific language, called Visual Imagery Reasoning Language (VIMRL), for reasoning about tasks in the ARC. We also demonstrate an innovative approach for how large search spaces can be decomposed using special high level functions that determine their own arguments through local searches on a given task item. Finally, we share our results obtained on the publicly available ARC items as well as our system's strong performance on a private test, recently tying for 4th place on the global ARCathon 2022 challenge.

翻译：抽象推理语料库（ARC）是一种用于衡量人工智能系统与人类流体智能的智力测试。本文提出一套用于推理并解决ARC任务的系统。该系统采用程序合成方法，在潜在程序空间中搜索能够解决ARC任务的目标程序，程序采用领域特定语言编写。在某些场景下，我们的搜索算法会参考真实程序语料库中的洞见。具体而言：我们提出一种名为视觉意象推理语言（VIMRL）的命令式领域特定语言，用于推理ARC任务；同时展示一种创新方法，通过特殊高级函数将大型搜索空间进行分解——这些函数能够在给定任务项上通过局部搜索自主确定其参数。最后，我们公开了在ARC可用项上取得的实验结果，以及系统在私有测试中的优异表现——近期在全球ARCathon 2022挑战赛中并列第四名。