  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.
