Eliciting collective behaviors through automatically generated environments
Eliciting collective behaviors through automatically generated environments
复制标题
通过自动生成的环境引发集体行为
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Dylan A. Shell
中科院分区：
文献类型：
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作者：
Benjamin T. Fine;Dylan A. Shell
许多智能体群体表现出突发的集体行为。代理运行的环境是导致行为的一个决定因素。这项工作展示了环境的自动列举如何能够探索执行有用的群体功能的各种集体行为(例如，隔离、围栏、形状形成)。虽然移动机器人等智能体可以通过显式控制来操纵，但这项研究表明，这些系统可以在不诉诸这种强制性手段的情况下有效地操纵。该方法对于异构型机器人系统，特别是包含大量简单智能体的机器人系统具有明显的应用价值。所介绍的方法是通用的，因为它作为输入：(1)环境生成的算法规范，(2)个体智能体控制规律的黑盒模型，(3)任务目标的数学描述。为了验证该方法的有效性，本研究研究了三种常用运动模型的两种行为(分裂和聚集)，其中包括著名的雷诺模型。仿真和物理多机器人试验表明，自动生成的环境可以从一组单独的代理中获得预先指定的行为。此外，这项工作还调查了群体的紧急属性对通过环境诱导特定行为的能力的影响。这些发现表明，自动探索环境可以更好地探索和理解集体行为，包括识别以前未知的紧急行为。
Many groups of agents exhibit emergent collective behaviors. The environment in which the agents operate is one determinant of the resulting behaviors. This work shows how automatic enumeration of environments enables exploration of various collective behaviors that perform useful group functions (e.g. segregation, corralling, shape formation). Although groups of agents, such as mobile robots, can be manipulated through explicit control, this study shows that these systems can be usefully manipulated without resorting to such imperative means. This method has obvious uses for heterogeneous robot systems, especially those which include large numbers of simple agents. The method introduced is general, in that it takes as input: (1) algorithmic specifications of the environment generation, (2) a black-box model of the individual agent's control laws, and (3) a mathematical description of the task objective. To show the validity of the proposed method this investigation studies two behaviors (splitting and corralling) for three commonly studied motion models, including the well known Reynold's model. Simulations and physical multi-robot trials show that automatically generated environments can elicit pre-specified behaviors from a group of individual agents. Additionally, this work investigates the effects of a group's emergent properties on the ability to elicit the specified behavior via the environment. The findings suggest that automatically exploring environments can lead to better exploration and understanding of collective behaviors, including the identification of previously unknown emergent behaviors.