竞争共进化的全球进展:替代方法的系统比较.
Stefano Nolfi1, Paolo Pagliuca1
1Laboratory of Autonomous Robotics and Artificial Life (LARAL), Institute of Cognitive Sciences and Technologies (ISTC), National Research Council (CNR), Rome, Italy.
Frontiers in robotics and AI
|February 5, 2025
概括
竞争性共同进化增强了适应性代理,但可能导致战略循环. 本研究介绍了衡量进步的方法,其中通用主义方法证明了在代理物进化中长期的全球进步.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 进化计算是一种进化计算.
背景情况:
- 竞争性的共同进化通过模拟环境变化来推动人工剂的适应.
- 一个常见的挑战是限制周期的出现,其中代理人反复循环通过策略发现和忘记.
研究的目的:
- 通过竞争性共同进化,研究合成逐步更好的解决方案的方法.
- 引入和评估用于测量代理演化的历史和全球进展的技术.
- 确定促进真正进步和克服当地的最佳条件的因素.
主要方法:
- 开发方法来衡量竞争性共同进化的历史和全球进展.
- 实现了算法,创建各种对手的档案来评估代理.
- 引入了用于识别和抛弃只导致局部进步的策略的技术.
- 在捕食者-猎物模拟中比较了四种算法,包括两种新的方法.
主要成果:
- 所有测试的算法都在掠食者-猎物场景中实现了长期的全球进展.
- 在算法之间的进步速度和进步与倒退的比率中观察到显著的差异.
- 新的通用主义方法始终优于其他算法,独特地实现了持续的全球进步.
结论:
- 有效的进度测量和对手存档对于成功的竞争共进化至关重要.
- 一般主义方法提供了一种强有力的方法,以实现适应性代理进化的真正和持续的全球进步.
- 需要仔细设计算法,以减轻限制周期并促进一致的进步.
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