探索与学习结构：导航智能体中的主动推断方法
Exploring and Learning Structure: Active Inference Approach in Navigational Agents
AI总结：
本文融合拓扑认知建图与主动推断框架 AIF，使导航智能体在无环境先验信息时，于单回合内低重叠地快速学习并扩展环境结构。
摘要：
受动物导航策略启发，我们提出了一种基于生物启发原理的新型导航与建图计算模型。动物通过高效利用记忆、想象和策略性决策，在复杂且存在感知混叠的环境中展现出卓越的导航能力。基于这些洞见，我们将传统认知建图方法与主动推断框架（Active Inference Framework，AIF）相结合，以在少数几步内学习环境结构。通过引入用于长期记忆的拓扑建图，以及用于导航规划和结构学习的 AIF，我们的模型能够动态理解环境结构，并在探索过程中利用预测信念扩展其内部地图。与克隆结构化图（Clone-Structured Graph，CSCG）模型的对比实验表明，我们的模型能够在单个回合中快速学习环境结构，并且导航重叠极小。这一能力是在没有环境维度或观测类型先验知识的情况下实现的，展示了其在模糊环境中导航的鲁棒性和有效性。
英文摘要：
Drawing inspiration from animal navigation strategies, we introduce a novel computational model for navigation and mapping, rooted in biologically inspired principles. Animals exhibit remarkable navigation abilities by efficiently using memory, imagination, and strategic decision-making to navigate complex and aliased environments. Building on these insights, we integrate traditional cognitive mapping approaches with an Active Inference Framework (AIF) to learn an environment structure in a few steps. Through the incorporation of topological mapping for long-term memory and AIF for navigation planning and structure learning, our model can dynamically apprehend environmental structures and expand its internal map with predicted beliefs during exploration. Comparative experiments with the Clone-Structured Graph (CSCG) model highlight our model's ability to rapidly learn environmental structures in a single episode, with minimal navigation overlap. this is achieved without prior knowledge of the dimensions of the environment or the type of observations, showcasing its robustness and effectiveness in navigating ambiguous environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
