在人类和机器规划中出现了类似的考虑失败
Alice Zhang1, Max Langenkamp2, Max Kleiman-Weiner3
1Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, United States of America; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, United States of America.
Cognition
|March 14, 2025
概括
人类和机器学习 (ML) 决策具有共同的失败模式:忽视明显的解决方案. 本研究探讨了ML架构如何在复杂问题解决中复制人类监督.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 决策 决策 决策 决策
背景情况:
- 人类擅长做出复杂的决策,通常依赖于有效的候选行动生成.
- 这个过程中的失败可能导致忽视可行的解决方案,这是人类解决问题的现象.
- 机器学习 (ML) 模型现在在象棋和围棋等领域竞争或超过人类表现.
研究的目的:
- 调查ML架构是否在开放式决策中表现出与人类相似的考虑失败.
- 了解导致这些失败的基础架构原则,无论是人类还是ML.
- 阐明人类和机器学习对复杂决策的方法之间的关系.
主要方法:
- 在开放式任务中对人类和ML决策过程的比较分析.
- 检查与人类认知过程相关的ML架构原则.
- 展示一个特定的案例,展示共享考虑失败.
主要成果:
- 类似于人类的ML架构可以在开放式决策中表现出"考虑失败".
- 这些失败发生在最佳解决方案没有产生或考虑时,尽管它们的明显价值.
- 人类和机器学习系统都显示出由于这些机制而导致的成功和失败的特征模式.
结论:
- 推动机器学习在复杂游戏中的成功的架构原则也可能导致类似人类的决策失败.
- 了解这些共同的失败为人类认知和人工智能发展提供了洞察力.
- 进一步的研究可以在复杂的问题解决方面弥合人类和人工智能之间的差距.
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