生物启发的智能系统:在不断变化的环境中,最小显现任务和最大潜伏系统之间的谈判
Stephen Fox1, Tapio Heikkilä1, Eric Halbach1
1VTT Technical Research Centre of Finland, FI-02150 Espoo, Finland.
Entropy (Basel, Switzerland)
|November 24, 2023
概括
系统智能通过最小化明显和最大化潜在来平衡稳定性和适应性. 这种方法优化了生产系统中的任务分配,提高了效率和适应性行为.
科学领域:
- 理论物理 理论物理
- 理论神经科学 理论神经科学
- 系统工程 系统工程
背景情况:
- 增加的智力与更高的度相关,使得适应性行为能够进入更多状态.
- 系统的运行需要效率,通过最小化明显的来实现,正如最小作用原理所示.
- 隐性代表了访问更多状态的潜力,这对适应性至关重要.
研究的目的:
- 将系统智能定义为内部稳定性和环境适应性之间的平衡.
- 探索自动化谈判在管理适应性和稳定性方面的作用.
- 提出一个数学模型来平衡潜在系统和明显任务.
主要方法:
- 开发了对系统智能的第一原则分析.
- 介绍了一个数学谈判模型来平衡隐性和明显的.
- 在生产系统中模拟了谈判模型.
主要成果:
- 系统智能的特点是尽量减少明显的任务,同时最大限度地提高潜在的系统.
- 自动化谈判有效地平衡了智能系统的适应性和稳定性.
- 基于最大化潜在系统的任务分配,在生产场景中最大限度地减少明显的任务.
结论:
- 拟议的模型为理解和增强系统智能提供了一个框架.
- 通过谈判平衡是高效和适应性系统的关键.
- 这些发现对优化生产系统和任务分配具有实际意义.
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