介绍ActiveInference.jl:使用主动推理模型进行模拟和参数估计的Julia库
Samuel William Nehrer1, Jonathan Ehrenreich Laursen1, Conor Heins2,3
1School of Culture and Communication, Aarhus University, 8000 Aarhus, Denmark.
我们介绍ActiveInference.jl,这是一个Julia包,用于使用部分可观测马尔科夫决策过程 (POMDP) 模型创建主动推理代理. 这个工具简化了认知科学和神经科学研究人员模拟和分析行为数据的过程.
科学领域:
- 计算神经科学是一种计算神经科学.
- 认知科学是一种认知科学.
- 计算精神病学是一种计算精神病学.
背景情况:
- 积极推断是理解代理人如何与环境相互作用的框架.
- 部分可观测的马尔科夫决策过程 (POMDP) 用于模拟不确定性下的决策.
- 现有的主动推理工具,如pymdp,主要在Python中提供.
研究的目的:
- 介绍ActiveInference.jl,这是一个新的软件包,用于Julia编程语言.
- 为朱莉亚研究社区提供POMDP生成模型的积极推理代理.
- 促进使用主动推理模型进行模拟,数据匹配和模型比较.
主要方法:
- 将Python的pymdp库重新实现到Julia的ActiveInference.jl中.
- 为认知和行为建模确保与现有的 Julia 库兼容.
- 使用采样和变异方法将POMDP主动推理模型与实证数据相匹配.
主要成果:
- ActiveInference.jl提供了一种直接的方式来构建POMDP主动推理模型.
- 该套件使研究人员能够轻松将模型与观察到的行为相匹配.
- 研究人员可以使用ActiveInference.jl进行模拟和模型比较.
结论:
- ActiveInference.jl降低了在 Julia.中使用 POMDPs 进行主动推理的进入障碍.
- 该软件包支持认知科学,神经科学和精神病学的先进计算建模.
- 这便于将理论主动推理框架与实证研究相结合.
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