发表机构
Eindhoven University of Technology; Lazy Dynamics（埃因霍温理工大学; 懒动力学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究探讨复杂推理在主动推理中的作用，通过在反应迷宫中评估，发现单独要素不足，复杂推理和全联合认知先验主动推理结合认知驱动与闭环推理解决环境问题，优势源于闭环形式，可在认知先验变分推理中表示。
AI 中文摘要
复杂推理是主动推理的一种变体，常与递归信念建模和树搜索相关。我们认为其核心计算作用更简单：在规划范围内，通过允许未来行动依赖未来状态和观察，使主动推理成为闭环。这种闭环结构可在认知先验变分自由能框架中表示。认知先验提供主动推理目标，而未来状态和行动的联合后验提供状态依赖控制结构。我们在反应迷宫中评估这种分解，该迷宫是用于区分认知激励与内循环闭环控制的随机基准。比较包括具有相同状态 - 行动后验族的三个变分目标、行动 - 状态分解的主动推理目标、复杂推理和标准期望自由能规划。结果表明，单独的任何一个要素都不足够。没有认知成分的方法不寻求信息，而阻止未来行动依赖未来状态的方法无法将信息转化为可靠的目标达成。相比之下，复杂推理和全联合认知先验主动推理通过将认知驱动与闭环推理相结合来解决环境问题。这些结果表明，与复杂推理相关的优势不必特定于树搜索本身。它源于主动推理的闭环形式，当后验使未来行动依赖未来状态时，这种形式可在认知先验变分推理中表示。
英文摘要
Sophisticated Inference is a variant of active inference often associated with recursive belief modeling and tree search. We argue that its central computational role is simpler: within a planning horizon, it makes active inference closed-loop by allowing future actions to depend on future states and observations. This closed-loop structure can be represented in the epistemic-prior variational free energy framework. Epistemic priors supply the active-inference objective, while a joint posterior over future states and actions supplies the state-contingent control structure. We evaluate this decomposition in the Reactivity Maze, a stochastic benchmark designed to separate epistemic incentive from inner-horizon closed-loop control. The comparison includes three variational objectives with the same state-action posterior family, an action-state factorized active inference objective, Sophisticated Inference, and standard Expected Free Energy planning. The results show that neither ingredient is sufficient on its own. Methods without an epistemic component do not seek information, while methods that prevent future actions from depending on future states cannot turn information into reliable goal-reaching. By contrast, both Sophisticated Inference and full-joint epistemic-prior active inference solve the environment by combining epistemic drive with closed-loop inference. These results show that the advantage associated with Sophisticated Inference need not be specific to tree search itself. It arises from the closed-loop form of active inference, and this form can be represented in epistemic-prior variational inference when the posterior keeps future actions dependent on future states.
