Solving Abstract Reasoning Tasks with Grammatical Evolution
Solving Abstract Reasoning Tasks with Grammatical Evolution
复制标题
用语法进化解决抽象推理任务
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
K. Morik
中科院分区：
文献类型：
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作者：
Raphael Fischer;Matthias Jakobs;Sascha Mücke;K. Morik
。包括基于图像的逻辑推理任务的抽象和推理语料库（ARC）旨在作为测量智能的基准。由于其多样性和较低的培训数据，因此很难解决这些任务，因此很难解决这些任务。我们在这里提出了我们的方法，该方法通过语法进化来解决特定于图像转换的域语言。通过这种方法，我们成功地参加了在线挑战，在900名参与者中排名前4％。
. The Abstraction and Reasoning Corpus (ARC) comprising image-based logical reasoning tasks is intended to serve as a benchmark for measuring intelligence. Solving these tasks is very difficult for off-the-shelf ML methods due to their diversity and low amount of training data. We here present our approach, which solves tasks via grammatical evolution on a domain-specific language for image transformations. With this approach, we successfully participated in an online challenge, scoring among the top 4% out of 900 participants.