一种受神经多样性启发的、利用视觉表象与程序合成求解抽象与推理语料库（ARC）的求解器
A Neurodiversity-Inspired Solver for the Abstraction \& Reasoning Corpus (ARC) Using Visual Imagery and Program Synthesis
AI总结：
本文提出一种受神经多样性启发的核心知识AI方法，结合视觉表象表征与树搜索程序合成，在ARC挑战中取得第四名，展示了灵活组合核心知识解决新任务的能力。
摘要：
关于物理对象的核心知识——例如其永久性、空间变换和相互作用——是人类和非人类动物生物智能最基本的构建模块之一。尽管某些领域（如视觉、自然语言处理）的AI技术近年来取得了显著进展，但目前尚无AI系统能够在灵活运用核心知识解决新任务方面匹敌人类能力。我们提出一种新的核心知识AI方法，该方法结合了：1）受人类心理表象能力启发、尤其在神经多样性个体研究中观察到的核心知识视觉表征；与2）基于树搜索的程序合成，用于灵活组合核心知识以即时形成新的推理策略。我们展示了该系统在极具挑战性的抽象与推理语料库（ARC）任务上的性能，并分享了来自公开ARC条目的实验结果，以及我们在2022年全球ARCathon挑战赛私有测试集上获得第四名的成绩。
英文摘要：
Core knowledge about physical objects -- e.g., their permanency, spatial transformations, and interactions -- is one of the most fundamental building blocks of biological intelligence across humans and non-human animals. While AI techniques in certain domains (e.g. vision, NLP) have advanced dramatically in recent years, no current AI systems can yet match human abilities in flexibly applying core knowledge to solve novel tasks. We propose a new AI approach to core knowledge that combines 1) visual representations of core knowledge inspired by human mental imagery abilities, especially as observed in studies of neurodivergent individuals; with 2) tree-search-based program synthesis for flexibly combining core knowledge to form new reasoning strategies on the fly. We demonstrate our system's performance on the very difficult Abstraction \& Reasoning Corpus (ARC) challenge, and we share experimental results from publicly available ARC items as well as from our 4th-place finish on the private test set during the 2022 global ARCathon challenge.
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。
