不确定世界中的机器人群体：可控适应性
Robot Swarms in an Uncertain World: Controllable Adaptability
摘要：
有一种信念认为，复杂性和混乱是适应性的必要条件。但生命每时每刻都在处理复杂性，没有工程师们所担心的混乱，而是通过调用目标导向的行为。目标可以编程。这就是为什么生物体给了我们实现机器人适应性的希望。在本文中，一个目标导向的，或编程，行为，与环境的不确定性相互作用的描述方法。我们建议减少个人行为的结构（目标，意图）和随机成分（实现目标的概率）的随机变量与名义值应用概率方法。这使我们能够使用归一化熵指数来检测系统状态，通过估计每个代理对群体行为的贡献。可能的组状态数为27。我们认为，适应有这27个国家之间的可能路径数量有限。路径和状态可以被编程，以便在适应任何特定的任务和条件之后，适应性永远不会涉及混乱。我们建议该模型的应用程序操作的机器人或其他设备在偏远和/或危险的地方。
英文摘要：
There is a belief that complexity and chaos are essential for adaptability. But life deals with complexity every moment, without the chaos that engineers fear so, by invoking goal-directed behaviour. Goals can be programmed. That is why living organisms give us hope to achieve adaptability in robots. In this paper a method for the description of a goal-directed, or programmed, behaviour, interacting with uncertainty of environment, is described. We suggest reducing the structural (goals, intentions) and stochastic components (probability to realise the goal) of individual behaviour to random variables with nominal values to apply probabilistic approach. This allowed us to use a Normalized Entropy Index to detect the system state by estimating the contribution of each agent to the group behaviour. The number of possible group states is 27. We argue that adaptation has a limited number of possible paths between these 27 states. Paths and states can be programmed so that after adjustment to any particular case of task and conditions, adaptability will never involve chaos. We suggest the application of the model to operation of robots or other devices in remote and/or dangerous places.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
