文化调节的人工智能:通过反向增强学习,隐含学习利他主义的文化价值观
Nigini Oliveira1, Jasmine Li1, Koosha Khalvati1
1Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington, United States of America.
PloS one
|December 9, 2025
概括
人工智能 (AI) 可以通过观察人类来学习文化价值观,像孩子一样适应其行为. 这项研究使用反向强化学习 (IRL) 使人工智能能够获得特定文化规范和利他主义.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 文化人类学 文化人类学
背景情况:
- 由于人类文化价值观的多样性,开发通用AI伦理很困难.
- 人工智能系统需要学习和适应特定文化规范和实践的方法.
研究的目的:
- 提出和测试一种方法,让人工智能代理人从人类观察中隐含地获得文化相关的价值观.
- 为了证明AI可以学习和概括特定文化行为的行为.
主要方法:
- 利用反向增强学习 (IRL) 让人工智能代理人从人类行为中推断奖励函数.
- 使用一个实时决策的在线游戏来观察两个文化群体之间的利他主义变化.
- 人工智能代理人通过观察人类受试者的利他主义行为来学习.
主要成果:
- 人工智能代理人成功地获得了反映特定人类文化群体平均行为的利他主义特征.
- 学习的人工智能行为一般化到需要利他主义判断的新场景.
- 证明AI能够从观察中学习典型的文化行为和价值观的能力.
结论:
- 反向增强学习 (IRL) 为人工智能提供了一种可行的方法来学习文化价值.
- 人工智能代理人可以被赋予适应不同文化背景的能力.
- 这种方法为文化意识的AI系统提供了概念验证.
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