论文标题
在受约束环境中的类似人类和高效导航的快速和缓慢思考结合思维
Combining Fast and Slow Thinking for Human-like and Efficient Navigation in Constrained Environments
论文作者
论文摘要
当前的AI系统缺乏几种重要的人类能力，例如适应性，可推广性，自我控制，一致性，常识和因果推理。我们认为，人类决策的现有认知理论，例如快速和缓慢的理论，可以提供有关如何将AI系统推向某些能力的见解。在本文中，我们提出了一个基于快速/慢求解器和元认知组件的一般体系结构。然后，我们为该体系结构实例的行为提出了实验结果，该实例是针对在受约束环境中进行决定的AI系统。我们展示了将快速和缓慢的决策方式结合在一起，使系统能够随着时间的流逝而发展，并从慢速思考中逐渐通过足够的经验发展，这极大地有助于决策质量，资源消耗和效率。
Current AI systems lack several important human capabilities, such as adaptability, generalizability, self-control, consistency, common sense, and causal reasoning. We believe that existing cognitive theories of human decision making, such as the thinking fast and slow theory, can provide insights on how to advance AI systems towards some of these capabilities. In this paper, we propose a general architecture that is based on fast/slow solvers and a metacognitive component. We then present experimental results on the behavior of an instance of this architecture, for AI systems that make decisions about navigating in a constrained environment. We show how combining the fast and slow decision modalities allows the system to evolve over time and gradually pass from slow to fast thinking with enough experience, and that this greatly helps in decision quality, resource consumption, and efficiency.
