Active inference offers a principled account of behavior as minimizing average sensory surprise over time. Applications of active inference to control problems have heretofore tended to focus on finite-horizon or discounted-surprise problems, despite deriving from the infinite-horizon, average-surprise imperative of the free-energy principle. Here we derive an infinite-horizon, average-surprise formulation of active inference from optimal control principles. Our formulation returns to the roots of active inference in neuroanatomy and neurophysiology, formally reconnecting active inference to optimal feedback control. Our formulation provides a unified objective functional for sensorimotor control and allows for reference states to vary over time.
翻译:主动性推断提供了一种原则性的行为描述,即随着时间推移将平均感官突袭降到最低程度。主动推断控制问题的应用到目前为止往往侧重于有限的正正正数或折扣的异常问题,尽管这是自由能源原则的无限正数、平均超额要求的结果。在这里,我们从最佳控制原则中得出了无穷正数、平均超量的主动推导公式。我们的配方返回神经切除术和神经生理学中主动推断的根源,正式将主动推断与最佳反馈控制联系起来。我们的配方为感官控制提供了一个统一的目标功能,并允许参考州随着时间的推移而变化。