较少可以是更多:忘记函数对基于人工智能的政策捕捉工具性能影响
Léandre Lavoie-Hudon1, Coralie Bureau1, Daniel Lafond2
1Université Laval, Quebec City, Canada.
概括
人类启发的遗忘通过使用训练窗口来优先考虑最近的数据来增强人工智能 (AI). 这提高了动态环境中的AI预测准确度,使AI系统更具适应性和相关性.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
- 机器学习 机器学习
背景情况:
- 人工智能系统需要适应动态环境.
- 人类的遗忘提供了适应性优势.
- 概念漂移在不断变化的环境中挑战人工智能.
研究的目的:
- 研究将人类启发的忘记功能集成到AI中.
- 在动态环境中提高AI适应性和性能.
- 地址概念通过优先考虑最近的信息而漂移.
主要方法:
- 在认知影子 (CS) AI系统中实现了一个训练窗口 (超参数).
- CS学习和模拟人类决策模型.
- 在三次会议中,在一场类似于石头,纸,剪刀的游戏中测试了AI性能.
主要成果:
- 培训窗口显著提高了AI预测准确性,在会话2和3.
- 通过培训窗口优先考虑最近的数据提高了绩效.
- 人工智能成功地预测并对抗人类的决策.
结论:
- 人类启发的遗忘机制可以提高AI的适应性.
- 培训窗口在缓解概念漂移方面是有效的.
- 这种方法对未来的人工智能决策支持系统有影响.
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