一个自主的人工智能代理用于通用行为分析
Almir Aljović1,2, Zuwan Lin1,3,2, Wenbo Wang1,3
1John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA.
bioRxiv : the preprint server for biology
|June 6, 2025
概括
BehaveAgent是一个自动化从视频中分析动物行为的人工智能. 这种自主系统使用人工智能来解释跨物种的复杂行为,而无需重新培训,使研究更容易获得.
科学领域:
- 神经科学是一个神经科学.
- 伦理学 伦理学 伦理学
- 心理学 心理学 心理学
背景情况:
- 行为分析是复杂的,需要专业知识和人工努力,限制了研究的可扩展性.
- 目前的方法需要手动注释,细分和特定任务的模型培训.
研究的目的:
- 介绍BehaveAgent,一个用于自动行为分析的自主多式联络人工智能代理.
- 在不需要再培训的情况下,实现跨物种可扩展,可概括和可解释的行为分析.
主要方法:
- 利用多式联运大型语言模型 (LLM) 实现零射击通用化.
- 集成了LLMs,视觉语言模型 (VLM) 和视觉接地模块.
- 采用多式联络记忆和目标导向注意力机制.
主要成果:
- BehaveAgent自主识别了各种物种 (从植物到人类) 的范式,主体和行为.
- 执行端到端分析,包括跟踪,姿势估计,细分和序列识别.
- 生成分析代码和综合文献整合的综合研究报告.
结论:
- BehaveAgent为生物和行为研究提供了一个可扩展,可概括和可解释的范式.
- 通过可解释的AI推理来减少行为分析所需的时间和专业知识.
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