In order to determine an optimal plan for a complex task, one often deals with dynamic and hierarchical relationships between several entities. Traditionally, such problems are tackled with optimal control, which relies on the optimization of cost functions; instead, a recent biologically-motivated proposal casts planning and control as an inference process. Active inference assumes that action and perception are two complementary aspects of life whereby the role of the former is to fulfill the predictions inferred by the latter. In this study, we present a solution, based on active inference, for complex control tasks. The proposed architecture exploits hybrid (discrete and continuous) processing, and it is based on three features: the representation of potential body configurations related to the objects of interest; the use of hierarchical relationships that enable the agent to flexibly expand its body schema for tool use; the definition of potential trajectories related to the agent's intentions, used to infer and plan with dynamic elements at different temporal scales. We evaluate this deep hybrid model on a habitual task: reaching a moving object after having picked a moving tool. We show that the model can tackle the presented task under different conditions. This study extends past work on planning as inference and advances an alternative direction to optimal control.

翻译：为复杂任务确定最优规划时，常需处理多个实体间的动态层次关系。传统方法采用最优控制理论，其依赖于成本函数的优化；而近期一项受生物学启发的提案则将规划与控制重构为推理过程。主动推断理论认为，行动与感知是生命活动的两个互补方面，前者的作用在于实现后者所推断的预测。本研究提出一种基于主动推断的复杂控制任务解决方案。该架构利用混合（离散与连续）处理机制，基于三大特征构建：与目标物体相关的潜在身体构型表征；使智能体能够灵活扩展其工具使用身体图式的层次关系；与智能体意图相关的潜在轨迹定义，用于在不同时间尺度上对动态元素进行推理与规划。我们在习惯性任务（拾取移动工具后触及移动物体）上评估该深度混合模型，证明其能在不同条件下处理所提出的任务。本研究拓展了既往将规划视为推理的研究工作，并为最优控制理论提供了替代方向。