Living organisms need to acquire both cognitive maps for learning the structure of the world and planning mechanisms able to deal with the challenges of navigating ambiguous environments. Although significant progress has been made in each of these areas independently, the best way to integrate them is an open research question. In this paper, we propose the integration of a statistical model of cognitive map formation within an active inference agent that supports planning under uncertainty. Specifically, we examine the clone-structured cognitive graph (CSCG) model of cognitive map formation and compare a naive clone graph agent with an active inference-driven clone graph agent, in three spatial navigation scenarios. Our findings demonstrate that while both agents are effective in simple scenarios, the active inference agent is more effective when planning in challenging scenarios, in which sensory observations provide ambiguous information about location.

翻译：生物体需要同时获取用于学习世界结构的认知地图，以及能够应对模糊环境导航挑战的规划机制。尽管这两个领域各自取得了显著进展，但如何最佳地整合它们仍是一个开放的研究问题。本文提出将认知地图形成的统计模型整合到支持不确定性下规划的主动推理框架中。具体而言，我们研究了认知地图形成的克隆结构化认知图（CSCG）模型，并在三种空间导航场景中比较了朴素克隆图智能体与主动推理驱动的克隆图智能体的表现。研究结果表明，虽然两种智能体在简单场景中均有效，但在感官观测提供模糊位置信息的挑战性场景中，主动推理智能体的规划效率更高。