Language models have demonstrated impressive capabilities across various
natural language processing tasks, yet they struggle with planning tasks
requiring multi-step simulations. Inspired by human cognitive processes, this
paper investigates the optimal planning power of language models that can
construct a cognitive map of a given environment. Our experiments demonstrate
that cognitive map significantly enhances the performance of both optimal and
reachable planning generation ability in the Gridworld path planning task. We
observe that our method showcases two key characteristics similar to human
cognition: \textbf{generalization of its planning ability to extrapolated
environments and rapid adaptation with limited training data.} We hope our
findings in the Gridworld task provide insights into modeling human cognitive
processes in language models, potentially leading to the development of more
advanced and robust systems that better resemble human cognition.

本文研究了语言模型的最佳规划能力，可以构建给定环境的认知地图，实验证明认知地图显著提高了网格世界路径规划任务中的规划生成能力，同时展示了与人类认知类似的两个关键特征：将规划能力泛化到外推环境和有限训练数据下的快速适应。希望本研究结果能够为基于语言模型的模拟人类认知流程提供启示，从而开发出更先进、更健壮的系统，更好地模拟人类思维。