由好奇心驱动的寻找新奇的不平衡行为
Martin J Falk1, Finnegan D Roach1, William Gilpin2
1Department of Physics, The University of Chicago, Chicago, Illinois 60637, USA.
概括
这项研究引入了一种新的主动和无监督学习方法,用于自动探索缺乏已知的顺序参数的复杂不平衡系统. 该方法反复地改进了对系统行为及其管理参数的理解,克服了一个常见的研究挑战.
科学领域:
- 复杂系统科学 复杂系统科学
- 机器学习 机器学习
- 统计物理 统计物理
背景情况:
- 在复杂的系统中探索各种各样的行为是具有挑战性的.
- 现有的采样技术往往需要预定义的顺序参数,这对于许多不平衡系统来说是未知的.
- 这就产生了一个"蛋"的问题,需要新的行为来定义参数,但需要参数来找到新的行为.
研究的目的:
- 开发一种自动化方法来探索具有未知顺序参数的不平衡系统.
- 结合主动学习和无监督学习,以有效地发现行为.
- 解决识别新型行为及其相应的顺序参数的挑战.
主要方法:
- 实施了一种代方法,将主动学习和无监督学习结合起来.
- 积极学习被用来扩展基于当前顺序参数的观察行为库.
- 使用无监督学习,从扩展的行为库中重新学习和完善顺序参数.
主要成果:
- 成功演示了Kuramoto复杂度不断增加的模型的方法.
- 重现已知的系统阶段,并确定以前未知的行为.
- 发现了与这些新型行为相关的新秩序参数.
结论:
- 提出的积极和无监督学习框架有效地自动化了复杂的不平衡系统的探索.
- 它克服了未知的顺序参数的限制,使得新的系统行为可以被发现.
- 该方法为在复杂系统探索中将自动搜索与人类直觉相结合提供了一条途径.
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