论文标题
主动推断或控制作为推理？统一的视图
Active Inference or Control as Inference? A Unifying View
论文作者
论文摘要
主动推论（AI）是计算神经科学的有说服力的理论框架，旨在将动作和感知描述为基于推理的计算。但是，该框架尚未提供实用的感觉运动控制算法，这些算法具有替代方法的竞争力。在这项工作中，我们通过控制镜头作为推理（CAI）进行主动推断，这是一种呈现轨迹优化为推理的工作体系。从“概率数字”的更广泛视图中，CAI提供了具有不确定性量化的原则性，数字上强大的最佳控制求解器，并且可以通过近似推断来扩展到非线性问题。我们表明，当在观察态中明确定义成本函数时，AI可能被构架为部分观察的CAI。
Active inference (AI) is a persuasive theoretical framework from computational neuroscience that seeks to describe action and perception as inference-based computation. However, this framework has yet to provide practical sensorimotor control algorithms that are competitive with alternative approaches. In this work, we frame active inference through the lens of control as inference (CaI), a body of work that presents trajectory optimization as inference. From the wider view of `probabilistic numerics', CaI offers principled, numerically robust optimal control solvers that provide uncertainty quantification, and can scale to nonlinear problems with approximate inference. We show that AI may be framed as partially-observed CaI when the cost function is defined specifically in the observation states.
