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首页> 外文会议>7th World Multiconference on Systemics, Cybernetics and Informatics(SCI 2003) vol.11: Communication, Network and Control Systems, Technologies and Applications: II >Self-referential Biological Inspiration: Humans Observing Human Swarms to Identify Swarm Programming Techniques
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Self-referential Biological Inspiration: Humans Observing Human Swarms to Identify Swarm Programming Techniques

机译：自指生物学灵感：人类观察人类群体以识别群编程技术

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摘要

Biology has elegantly solved numerous engineering problems that are far beyond our current collective abilities. Taking the apprentice role, with Nature as the master crafter, scientists and engineers can learn much by examining organic solutions. A biologically-inspired approach typically comes after trying unsuccessfully to tackle a specific problem using classical methods. Observing relatively simple creatures that exhibit the necessary abilities in their natural habitat often yield new and successful approaches. Turning this approach on its head, our research looks at higher-level creatures (humans) without a specific problem in hand. Our strategy is to create a crucible from which we can extract elemental components of complex behaviors to develop a toolbox of general programming techniques for multi-robot control algorithms. By conducting multiple observations of human swarms performing constrained experiments, patterns of successful behavior emerge. Using these patterns, human swarm algorithms have been reconstructed and applied to simulated robots. Another benefit of using human swarms is the high-level communication available for giving commands (it is easy to tell humans what to do). Unfortunately, this can also make it difficult to reverse engineer human swarm solutions. It is hard to identify and quantify use of hand or voice signals, subtle cues from body language and facial expressions. So, to support algorithm mining, we have developed a prototype system for implementing virtual human swarms. The distributed system controls what a human at a remote keyboard sees and hears, as well as, restricting and monitoring their communication. For example, the software can enforce reduced sensor capabilities, prevent global communication, and limit possible agent actions. The virtual swarm software can record all actions and communications during an experiment facilitating algorithm extraction and the cataloging of swarm programming component behaviors.
机译：生物学已经优雅地解决了许多工程问题，这些问题远远超出了我们目前的集体能力。承担学徒的角色，以自然界为主要制作者，科学家和工程师可以通过研究有机解决方案来学到很多东西。受到生物学启发的方法通常是在尝试使用经典方法未能成功解决特定问题之后出现的。观察相对简单的生物，它们在自然栖息地中展现出必要的能力，通常会产生新的成功方法。反过来，我们的研究着眼于没有特定问题的高级生物（人类）。我们的策略是创建一个坩埚，从中可以提取复杂行为的元素，以开发用于多机器人控制算法的通用编程技术工具箱。通过对执行约束实验的人类群体进行多次观察，可以得出成功行为的模式。使用这些模式，人类群算法已被重建并应用于模拟机器人。使用人员群体的另一个好处是可用于发出命令的高级通信（很容易告诉人们该怎么做）。不幸的是，这也可能使逆向工程人类解决方案变得困难。很难识别和量化手或语音信号的使用，以及肢体语言和面部表情的细微提示。因此，为了支持算法挖掘，我们开发了一个用于实现虚拟人类群的原型系统。分布式系统控制远程键盘上的人员看到和听到的内容，以及限制和监视其通信。例如，该软件可以强制降低传感器的功能，阻止全局通信并限制可能的座席操作。虚拟群软件可以在实验过程中记录所有动作和通讯，从而促进算法提取和群编程组件行为的分类。

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