By dynamic planning, we refer to the ability of the human brain to infer and impose motor trajectories related to cognitive decisions. A recent paradigm, active inference, brings fundamental insights into the adaptation of biological organisms, constantly striving to minimize prediction errors to restrict themselves to life-compatible states. Over the past years, many studies have shown how human and animal behavior could be explained in terms of an active inferential process -- either as discrete decision-making or continuous motor control -- inspiring innovative solutions in robotics and artificial intelligence. Still, the literature lacks a comprehensive outlook on how to effectively plan actions in changing environments. Setting ourselves the goal of modeling tool use, we delve into the topic of dynamic planning in active inference, keeping in mind two crucial aspects of biological goal-directed behavior: the capacity to understand and exploit affordances for object manipulation, and to learn the hierarchical interactions between the self and the environment, including other agents. We start from a simple unit and gradually describe more advanced structures, comparing recently proposed design choices and providing basic examples for each section. This study distances itself from traditional views centered on neural networks and reinforcement learning, and points toward a yet unexplored direction in active inference: hybrid representations in hierarchical models.

翻译：通过动态规划，我们指人脑推断并施加与认知决策相关的运动轨迹的能力。近年兴起的主动推理范式为生物有机体的适应机制提供了基础性洞见——该机制不断致力于最小化预测误差，以将自身限制在生命兼容的状态内。过去数年间，大量研究表明，人类及动物的行为可被阐释为主动推理过程（无论是离散决策还是连续运动控制），这为机器人与人工智能领域启发了创新性解决方案。然而，现有文献尚缺乏在动态环境中有效规划行动的综合视角。以建模工具使用为目标，我们深入探究主动推理中的动态规划课题，重点关注生物目标导向行为的两个关键维度：理解并利用可供性进行物体操纵的能力，以及学习自我与环境（包括其他智能体）之间层次化交互的能力。我们从基础单元出发，逐步描述更复杂的结构，比较近期提出的设计方案，并为每个章节提供基础示例。本研究脱离传统以神经网络和强化学习为核心的视角，指向主动推理中尚未被探索的方向：层次化模型中的混合表征。