揭示主动推理中信息探索与目标导向行为的统一机制
What Type of Inference is Active Inference?
- 将主动推理的预期自由能转化为带认知先验的变分自由能
- 证明完整规划需同时修正认知熵与策略优化偏差
- 在网格世界实验中验证完整框架优于删减版本
主动推理将决策视为推断，其预期自由能（EFE）统一了目标导向与信息探索行为。近期研究发现，EFE最小化可等价为在引入认知先验的生成模型上进行变分自由能（VFE）最小化。本文证明该增广模型的VFE可重写为预测模型VFE加上显式的熵修正项，使EFE贡献清晰可见。进一步表明，正确的EFE规划需结合认知修正与规划修正，将边际推断转为策略优化，从而实现对基于EFE规划的完整变分表征。这明确了交叉熵规划与完整EFE规划所需的修正类型。相同熵修正形式还导出详细的消息传递算法及简化变体。在三个网格世界环境中，完整EFE规划性能显著优于省略规划修正或认知修正的变体。
原文摘要 · Abstract (English)
Active inference casts decision-making as inference, with the Expected Free Energy (EFE) unifying goal-directed and information-seeking behavior. Recent work showed that EFE minimization can be written as Variational Free Energy (VFE) minimization on a generative model augmented with epistemic priors. We prove that the VFE of the augmented model can be rewritten as the VFE of the predictive model plus explicit entropy-correction terms, making the EFE contribution transparent. We then show that proper EFE-based planning requires combining these epistemic corrections with a planning correction that turns marginal inference into policy optimization, yielding a full variational characterization of EFE-based planning. This clarifies which corrections are needed for cross-entropy planning and for full EFE-based planning. The same entropy-corrected formulation leads to a detailed message-passing scheme for EFE-based planning together with simpler ablations. Experiments on three grid-world environments show that full EFE-based planning outperforms ablations that omit either the planning correction or the epistemic corrections.
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