Active inference is a normative principle underwriting perception, action,
planning, decision-making and learning in biological or artificial agents. From
its inception, its associated process theory has grown to incorporate complex
generative models, enabling simulation of a wide range of complex behaviours.
Due to successive developments in active inference, it is often difficult to
see how its underlying principle relates to process theories and practical
implementation. In this paper, we try to bridge this gap by providing a
complete mathematical synthesis of active inference on discrete state-space
models. This technical summary provides an overview of the theory, derives
neuronal dynamics from first principles and relates this dynamics to biological
processes. Furthermore, this paper provides a fundamental building block needed
to understand active inference for mixed generative models; allowing continuous
sensations to inform discrete representations. This paper may be used as
follows: to guide research towards outstanding challenges, a practical guide on
how to implement active inference to simulate experimental behaviour, or a
pointer towards various in-silico neurophysiological responses that may be used
to make empirical predictions.

本篇论文提供了关于 Active Inference 在离散状态空间模型中的完整数学综合，从基本原理推导神经动力学，并将此动力学与生物过程联系起来，是了解混合生成模型中的主动推理所需的基本构建块。