In many self-organising systems the ability to extract necessary resources from the external environment is essential to the system's growth and survival. Examples include the extraction of sunlight and nutrients in organic plants, of monetary income in business organisations and of mobile robots in swarm intelligence actions. When operating within competitive, ever-changing environments, such systems must distribute their internal assets wisely so as to improve and adapt their ability to extract available resources. As the system size increases, the asset-distribution process often gets organised around a multi-scale control topology. This topology may be static (fixed) or dynamic (enabling growth and structural adaptation) depending on the system's internal constraints and adaptive mechanisms. In this paper, we expand on a plant-inspired asset-distribution model and introduce a more general multi-scale model applicable across a wider range of natural and artificial system domains. We study the impact that the topology of the multi-scale control process has upon the system's ability to self-adapt asset distribution when resource availability changes within the environment. Results show how different topological characteristics and different competition levels between system branches impact overall system profitability, adaptation delays and disturbances when environmental changes occur. These findings provide a basis for system designers to select the most suitable topology and configuration for their particular application and execution environment.
翻译:在许多自我组织系统中,从外部环境中提取必要资源的能力对于系统的增长和生存至关重要,例如有机植物中的阳光和养分提取、商业组织中的货币收入和流动机器人在群温智能行动中的吸收、商业组织中的货币收入和流动机器人的提取。当在竞争激烈、不断变化的环境中运作时,这些系统必须明智地分配其内部资产,以便提高和调整其获取现有资源的能力。随着系统规模的扩大,资产分配过程往往围绕一个多尺度的控制表层进行。这种表层学可能是静止的(固定的)或动态的(扶持增长和结构适应的),取决于系统的内部制约和适应机制。在本文件中,我们扩展了一种受植物启发的资产分配模式,并引入了一种更为普遍的多尺度模型,适用于更广泛的自然和人工系统领域。我们研究了多尺度控制过程的表面学对系统在环境内资源可得性变化时进行自我调适资产分配的能力的影响。结果显示,系统各部门之间不同的表层特征和不同的竞争水平如何影响整个系统的盈利能力、适应性拖延和适应性。在环境变化时,这些结果为最合适的环境变化提供了一种选择环境配置基础。