Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235-1679
Proc Natl Acad Sci U S A. 2020 Nov 24;117(47):29390-29397. doi: 10.1073/pnas.1912335117.
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.
从诺贝尔奖得主物理学家如何做出发现到孩子们如何理解睡前故事，人们对视觉意象在人类智力中的作用有很多观察。这些观察为认知科学提出了一个重要问题，即当人们使用视觉意象时，他们的大脑中正在进行什么计算？回答这个问题并不容易，需要认知科学的多个学科继续进行大量研究。在这里，我们从人工智能 (AI) 的角度关注一个相关但范围更窄的问题：如果您有一个使用基于视觉意象的知识表示和推理操作的智能代理，那么可能会解决什么样的问题，以及这种问题解决方式将如何工作？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，研究了基于意象的人工智能代理如何解决视觉空间智能测试的案例。具体来说，我们首先研究了几种基于意象的知识表示和问题解决策略的变体，这些变体足以解决瑞文渐进矩阵智力测验中的问题。然后，我们研究了人工智能代理如何从经验中学习自己的部分知识和推理过程，包括学习视觉空间领域知识、学习和推广问题解决策略，以及首先学习任务的实际定义。
