关于自适应布尔网络机器人的关键性
Michele Braccini1, Andrea Roli1,2, Edoardo Barbieri1
1Department of Computer Science and Engineering, Università di Bologna, Campus of Cesena, I-47521 Cesena, Italy.
Entropy (Basel, Switzerland)
|July 8, 2023
概括
由关键布尔网络控制的机器人表现出增强的性能和适应能力. 机器人控制系统的动态关键性为在线适应和改进的指标提供了优势.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 复杂的系统复杂的系统.
- 人工智能的人工智能
背景情况:
- 系统中的动态关键性平衡了稳定性和响应性.
- 布尔网络用于人工分类器和机器人控制.
- 在线适应允许机器人随着时间的推移提高性能.
研究的目的:
- 调查动态关键性在线机器人适应中的作用.
- 检查布尔网络的适应如何影响机器人的性能.
- 通过合变化进行适应,与结构变化进行比较.
主要方法:
- 研究了由随机布尔网络 (RBNs) 控制的机器人.
- 在它们的传感器/执行器合,结构或两者之间进行了RBN的调整.
- 根据不同的适应策略,对机器人的性能指标进行了评估.
- 分析了动态模式 (有序,关键,无序).
主要成果:
- 关键的RBN控制机器人实现了更高的平均和最大性能.
- 通过合变化进行的适应通常比结构性变化产生了稍微更好的性能.
- 结构调整的有序网络往往会达到关键状态.
- 批判性可以提高机器人的适应性和性能.
结论:
- 动态关键性是机器人在线适应的优势.
- 在关键状态下校准机器人控制系统是有益的.
- 关键制度有助于复杂系统的适应.
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