论文标题
图形，约束和搜索抽象和推理语料库
Graphs, Constraints, and Search for the Abstraction and Reasoning Corpus
论文作者
论文摘要
抽象和推理语料库（ARC）旨在基准一般人工智能算法的性能。 ARC对广泛的概括和很少的学习学习的关注使使用纯机器学习很难解决。一种更有希望的方法是在适当设计的特定域语言（DSL）中执行程序合成。但是，这些也取得了有限的成功。我们使用图形摘要（ARGA）提出了抽象推理，这是一种新的以对象为中心的框架，首先使用图形表示图像，然后在DSL中对基于抽象图形空间的正确程序进行搜索。通过使用约束获取，状态哈希和禁忌搜索，这种组合搜索的复杂性被驯服。一组广泛的实验表明，Arga在竞争中效率很高，可以有效地解决弧线的一些复杂的以对象为中心的任务，从而产生正确且易于理解的程序。
The Abstraction and Reasoning Corpus (ARC) aims at benchmarking the performance of general artificial intelligence algorithms. The ARC's focus on broad generalization and few-shot learning has made it difficult to solve using pure machine learning. A more promising approach has been to perform program synthesis within an appropriately designed Domain Specific Language (DSL). However, these too have seen limited success. We propose Abstract Reasoning with Graph Abstractions (ARGA), a new object-centric framework that first represents images using graphs and then performs a search for a correct program in a DSL that is based on the abstracted graph space. The complexity of this combinatorial search is tamed through the use of constraint acquisition, state hashing, and Tabu search. An extensive set of experiments demonstrates the promise of ARGA in tackling some of the complicated object-centric tasks of the ARC rather efficiently, producing programs that are correct and easy to understand.
