一个好的预测是什么? 评估代理人的知识的挑战.
Alex Kearney1,2, Anna J Koop1, Patrick M Pilarski1,2,3,4
1Department of Computing Science, University of Alberta, Edmonton, AB, Canada.
Adaptive behavior
|June 7, 2023
概括
评估人工智能 (AI) 模型需要的不仅仅是准确性. 本研究引入了一种新的方法,通过检查内部学习过程来评估AI知识,重点关注更好的预测任务的功能相关性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 强化学习是一种强化学习.
背景情况:
- 通过任务独立的世界模型获得一般知识对人工智能代理至关重要.
- 当前的评估方法通常依赖于估计器的准确性,这可能不反映真正的有用性.
- 评估所学知识的实用性仍然是人工智能研究的一个重大挑战.
研究的目的:
- 突出使用估计器准确度来评估AI知识的局限性.
- 为持续学习环境中人工智能模型提出一种新的评估方法.
- 引入一种评估预测知识有用性的方法.
主要方法:
- 在Minecraft中使用思想实验和经验示例证明了准确性和有用性之间的冲突.
- 使用通用价值函数 (GVF) 框架进行分析.
- 建议通过检查内部学习过程来评估AI知识,特别是特征相关性.
主要成果:
- 一个模型的准确性并不总是与它对代理商的实际有用性相关.
- 提出了一种基于特征与预测任务相关性的新评估指标.
- 该研究为评估在线持续学习中的知识实用性提供了一个框架.
结论:
- 重新思考人工智能模型评估超出了简单的准确性是必不可少的.
- 根据它们的实用性和内部学习过程来评估预测,可以提供更强大的评估.
- 这项工作开创了对人工智能预测知识的"通过使用评估"的探索.
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