论文标题
从控制原理中得出时间平均的活动推断
Deriving time-averaged active inference from control principles
论文作者
论文摘要
Active推论提供了一个原则上的行为描述，可以最大程度地减少随着时间的推移的平均感觉惊喜。尽管源自无限 - 胜利，但自由能原则的平均赛车要求，但主动推断对控制问题的应用却倾向于专注于有限的 - 摩尼斯或折扣暴行问题。在这里，我们得出了一个无限 - 摩尼子，从最佳控制原理中进行主动推断的平均暴发表述。我们的表述恢复为神经解剖学和神经生理学的积极推断的根源，正式将主动推断重新连接至最佳反馈控制。我们的公式为感觉运动控制提供了统一的目标功能，并允许参考状态随时间变化。
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.
