一个通用的自学情感框架,用于机器
Alberto Hernández-Marcos1, Eduardo Ros2
1Research Centre for Information and Communications Technologies (CITIC-UGR) - Department of Computer Engineering, Automation, and Robotics (ICAR), University of Granada, Granada, 18071, Spain. albertoh@correo.ugr.es.
Scientific reports
|October 29, 2024
概括
研究人员开发了一个自我学习的人工智能框架,其中情绪是环境价值的时间模式. 这种人工智能成功地识别了八种基本情绪,与人类的感知和心理学保持一致.
科学领域:
- 人工智能的人工智能
- 认知神经科学 认知神经科学
- 计算心理学 计算心理学
背景情况:
- 智能生物利用情绪进行行为调节和进化优势.
- 当前的人工智能缺乏情感诱导的第一原则理论,导致了通用化模型.
- 现有的AI情感模型通常是具体的,缺乏广泛的适用性.
研究的目的:
- 提出一种自学的人工智能框架,用于从原始观测中引起情绪.
- 将情绪定义为对关键环境价值观的感知时间模式.
- 开发出能够表现和识别自然情绪的AI代理.
主要方法:
- 为人工智能代理人开发了一个完全自学的情感框架.
- 在未标记的代理体验上训练了一个人工神经网络.
- 通过使用人类观察者对快乐-兴奋-主导度维度的评分来验证框架.
主要成果:
- 人工智能框架成功地学习并识别了八种基本的情绪模式.
- 识别的情绪是局势上连贯的,并重现了自然的情绪动态.
- 人类观察者表现出高度的统计一致性,并与实验心理学保持一致.
结论:
- 拟议的框架提供了一种基于强化学习的人工情绪的通用,跨学科的方法.
- 这项研究可能为更像人类的情感AI铺平了道路.
- 这些发现表明,情绪可以被理解为环境相互作用中的感知时间模式.
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