从基础原理出发，重新构建离散状态下的主动推理机制。
Active Inference in Discrete State Spaces from First Principles
- 用约束发散最小化重构主动推理，无需依赖自由能原理
- 感知阶段等价于变分自由能，动作阶段引入熵正则项
- 为认知建模提供不依赖自由能假说的新框架，适合理论研究者
本文旨在澄清主动推理的概念，将其与自由能原理分离。我们表明，在离散状态空间中实现主动推理所需的优化，可被表述为约束发散最小化问题，可通过标准均场方法求解，且不依赖预期自由能的概念。当用于建模感知时，所提出的感知/动作发散准则等价于变分自由能；当用于建模动作时，其形式区别于预期自由能泛函，仅多出一个熵正则项。
原文摘要 · Abstract (English)
We seek to clarify the concept of active inference by disentangling it from the Free Energy Principle. We show how the optimizations that need to be carried out in order to implement active inference in discrete state spaces can be formulated as constrained divergence minimization problems which can be solved by standard mean field methods that do not appeal to the idea of expected free energy. When it is used to model perception, the perception/action divergence criterion that we propose coincides with variational free energy. When it is used to model action, it differs from an expected free energy functional by an entropy regularizer.
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