AI, visual imagery, and a case study on the challenges posed by human intelligence tests
AI, visual imagery, and a case study on the challenges posed by human intelligence tests
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DOI：
10.1073/pnas.1912335117
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发表时间：
2020-11-24
影响因子：
11.1
通讯作者：
Kunda, Maithilee
中科院分区：
文献类型：
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作者：
Kunda, Maithilee
关于视觉图像在人类智力中的力量，从诺贝尔奖获得者物理学家如何发现到儿童如何理解睡前故事，观察比比皆是。这些观察结果为认知科学提出了一个重要的问题，即当人们使用视觉图像时，他们的大脑中发生了什么计算？回答这个问题并不容易，需要在认知科学的多个学科中进行大量的持续研究。在这里，我们从人工智能（AI）的角度关注一个相关的、更受限制的问题：如果你有一个智能代理，它使用基于视觉图像的知识表示和推理操作，那么什么样的问题解决是可能的，以及这样的问题解决是如何工作的？我们强调了人工智能在视觉空间推理领域回答这些问题的最新进展，并研究了基于图像的人工智能如何解决视觉空间智能测试的案例研究。特别是，我们首先研究了几种变化的基于图像的知识表示和解决问题的策略，足以解决问题的乌鸦的进步矩阵智力测试。然后，我们将研究人工智能体如何从经验中学习自己的知识和推理过程，包括学习视觉空间领域知识，学习和概括解决问题的策略，以及首先学习任务的实际定义，而不是由人工智能研究人员手动设计。
Observations abound about the power of visual imagery in human intelligence, from how Nobel prize-winning physicists make their discoveries to how children understand bedtime stories. These observations raise an important question for cognitive science, which is, what are the computations taking place in someone's mind when they use visual imagery? Answering this question is not easy and will require much continued research across the multiple disciplines of cognitive science. Here, we focus on a related and more circumscribed question from the perspective of artificial intelligence (AI): If you have an intelligent agent that uses visual imagery-based knowledge representations and reasoning operations, then what kinds of problem solving might be possible, and how would such problem solving work? We highlight recent progress in AI toward answering these questions in the domain of visuospatial reasoning, looking at a case study of how imagery-based artificial agents can solve visuospatial intelligence tests. In particular, we first examine several variations of imagery-based knowledge representations and problem-solving strategies that are sufficient for solving problems from the Raven's Progressive Matrices intelligence test. We then look at how artificial agents, instead of being designed manually by AI researchers, might learn portions of their own knowledge and reasoning procedures from experience, including learning visuospatial domain knowledge, learning and generalizing problem-solving strategies, and learning the actual definition of the task in the first place.