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重构预期的自由能源:四个公式和一个统一.
Théophile Champion1, Howard Bowman2,3, Dimitrije Marković4
1University of Birmingham, School of Psychology and School of Computer Science, Birmingham B15 2TT, UKtxc314@alumni.bham.ac.uk.
Neural computation
|February 4, 2026
概括
积极推断通过分析两个定义来统一预期的自由能量配方. 这项研究澄清了在部分可观测的马尔科夫决策过程 (POMDP) 中对先前偏好的数学理由和约束.
科学领域:
- 计算神经科学是一种神经科学.
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
- 心理学 心理学 心理学
背景情况:
- 积极推理是跨多个科学学科的感知,学习和决策的统一理论.
- 它依赖于预期的自由能量,现有的公式如风险/模糊性和信息获取缺乏统一的衍生.
- 自由能源原则为理解生物和人工系统中的这些过程提供了一个基本框架.
研究的目的:
- 从单个根定义中推导出各种预期的自由能量配方的统一问题.
- 分析两种不同的方法来定义预期的自由能量及其数学理由.
- 调查概率约束对部分可观测的马尔科夫决策过程 (POMDPs) 中先前偏好的影响.
主要方法:
- 对预期的自由能量的两个主要定义的分析:对观测加模糊性的风险和对状态加模糊性的风险.
- 对不同预期的自由能量配方可以回收的条件进行数学检查.
- 证明概率约束如何影响POMDP中对观察的先前偏好的选择.
主要成果:
- 第一个定义 (观察风险加含糊性) 允许在特定的概率假设下恢复所有配方,但限制了POMDP中的先前偏好.
- 第二个定义 (风险超过状态加模糊性) 提供了理由,但只考虑了两个特定的表述 (风险超过状态加模糊性和加预期能量).
- 一个有限的类别的先前偏好比观察是兼容的生成模型的概率映射在POMDPs.
结论:
- 统一预期的自由能量公式需要仔细考虑所选择的定义及其基本假设.
- 概率约束在确定有效的先行偏好方面发挥着关键作用,特别是在像POMDPs这样的复杂系统中.
- 需要进一步的研究,以便在更广泛的自由能源原则中完全协调不同的预期自由能源配方,特别是对于没有随机波动的系统.
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