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.

本文提出了一种将统计模型的认知地图形成与支持不确定性规划的主动推理代理进行集成的方法，并通过在三个空间导航场景中比较原始克隆图代理和主动推理驱动的克隆图代理，证明了尽管两种代理在简单场景中都有效，但主动推理代理在具有关于位置的模糊信息的挑战性场景中的规划更有效。

在模糊环境中整合认知地图学习与主动推理的规划

Integrating cognitive map learning and active inference for planning in  ambiguous environments

Understanding how perception and action deal with sensorimotor conflicts,
such as the rubber-hand illusion (RHI), is essential to understand how the body
adapts to uncertain situations. Recent results in humans have shown that the
RHI not only produces a change in the perceived arm location, but also causes
involuntary forces. Here, we describe a deep active inference agent in a
virtual environment, which we subjected to the RHI, that is able to account for
these results. We show that our model, which deals with visual high-dimensional
inputs, produces similar perceptual and force patterns to those found in
humans.

研究人员通过在虚拟环境中用深度主动推理代理进行实验，发现橡胶手幻觉 (RHI) 不仅可以改变感知的手臂位置，还可以引起意志力无法控制的力量，并且该模型能够解释这些结果。