在决策过程中使用分布稳定的自由能源原则
Allahkaram Shafiei1, Hozefa Jesawada2, Karl Friston3
1Czech Technical University, Prague, Czechia.
Nature communications
|December 17, 2025
概括
我们推出了一个新的模型,即分布稳定的自由能量 (DR-FREE),以使自主代理更加稳健. 这种方法有助于代理商在条件意外变化时也可靠地执行,克服了当前AI模型的局限性.
科学领域:
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
- 机器学习 机器学习
背景情况:
- 自主代理人表现出高性能,但与不一致的培训和环境条件作斗争.
- 这种缺乏稳定性导致不良行为和失败,阻碍了现实世界的部署.
- 对于可靠的智能代理来说,解决培训环境的模糊性至关重要.
研究的目的:
- 引入一种新型模型,即分布稳固的自由能量 (DR-FREE),旨在在自主代理中内在灌输稳固性.
- 通过设计整合稳健性来增强决策机制.
- 在模两可或不断变化的环境中改进剂的性能.
主要方法:
- 开发了分布稳固的自由能源 (DR-FREE) 模型.
- 结合了自由能源原则的强大扩展与分辨率引擎.
- 直接融入到代理人的决策过程中.
主要成果:
- 在基准实验中,DR-FREE在自主代理中表现出卓越的稳定性.
- 配备DR-FREE的代理人成功完成了由于环境不一致而导致最先进模型失败的任务.
- 该模型有效地处理了培训环境的模两可.
结论:
- 对于面临环境不确定性的自主代理,DR-FREE提供了一个强大的解决方案.
- 这种方法可以在多代理系统和复杂环境中实现更广泛的部署.
- 这些发现提供了关于自然物质如何在最少的训练下适应不可预测的条件的见解.
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