在细胞自动机中发现传感运动代理,使用多样性搜索
Gautier Hamon1, Mayalen Etcheverry1,2, Bert Wang-Chak Chan3
1INRIA, University of Bordeaux, Talence 33405, France.
Science advances
|October 31, 2025
概括
研究人员使用机器学习在细胞自动机 (CA) 中找到允许"个体"自我组织的规则. 这些代理人表现出基本的代理人,在模拟环境中移动和强烈反应.
科学领域:
- 人工生命的人工生命
- 计算生物学 计算生物学
- 机器学习 机器学习
背景情况:
- 人工生命 (ALife) 在模拟中探索类似生命行为的自我组织.
- 蜂自动机 (CA) 的一个关键挑战是为没有预定义结构的自我组织强大的代理制造环境规则.
研究的目的:
- 在CA中自动搜索环境规则,从而导致代理类结构的自我组织.
- 从简单的规则中调查代理,感知和行动的出现.
主要方法:
- 杆式机器学习算法:多样性搜索,课程学习和梯度下降.
- 在CA模拟中系统地搜索环境条件.
- 专注于自动化自我组织个体的发现.
主要成果:
- 成功地确定了CA的环境条件,促进了基本机构的自我组织.
- 观察到的局部结构表现出连贯的运动,对障碍物的反应和完整性维护.
- 在新环境中表现出强大的概括能力.
结论:
- 开发的机器学习方法可以在CA中系统地发现自我组织的代理.
- 这项研究为人工智能和合成生物工程开辟了新的途径.
- 突出了从简单的计算规则中产生新兴代理和强度的潜力.
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