摘要翻译：
我们引入了一个新的设置，其中一群智能体，每个智能体都是由一个有限状态系统建模的，是统一控制的：控制器对每个智能体施加相同的动作。该框架主要受生物系统控制的启发，即酵母群体，控制器只能改变所有细胞共同的环境。我们研究了一类群体的同步问题：无论个体Agent对控制器的行为如何反应，控制器的目标是将所有Agent同步驱动到目标状态。智能体自然由一个非确定性有限状态自动机(NFA)表示，每个智能体都是如此，整个系统被编码为一个2人博弈。第一个参与者（控制器）选择行动，第二个参与者（代理）解决每个代理的不确定性。有m个代理的博弈称为m人口博弈。这就产生了一个参数化的控制问题（其中控制指2人博弈），即种群控制问题：控制器能控制N中所有m人的m-种群博弈吗？
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英文标题：
《Controlling a population》
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作者：
Nathalie Bertrand and Miheer Dewaskar and Blaise Genest and Hugo
  Gimbert and Adwait Amit Godbole
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最新提交年份：
2019
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Formal Languages and Automata Theory        形式语言与自动机理论
分类描述：Covers automata theory, formal language theory, grammars, and combinatorics on words. This roughly corresponds to ACM Subject Classes F.1.1, and F.4.3. Papers dealing with computational complexity should go to cs.CC; papers dealing with logic should go to cs.LO.
涵盖自动机理论，形式语言理论，文法，和词的组合学。这大致相当于ACM主题类F.1.1和F.4.3。处理计算复杂性的论文应该上CS.CC；处理逻辑的论文应该去CS.LO。
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一级分类：Computer Science        计算机科学
二级分类：Systems and Control        系统与控制
分类描述：cs.SY is an alias for eess.SY. This section includes theoretical and experimental research covering all facets of automatic control systems. The section is focused on methods of control system analysis and design using tools of modeling, simulation and optimization. Specific areas of research include nonlinear, distributed, adaptive, stochastic and robust control in addition to hybrid and discrete event systems. Application areas include automotive and aerospace control systems, network control, biological systems, multiagent and cooperative control, robotics, reinforcement learning, sensor networks, control of cyber-physical and energy-related systems, and control of computing systems.
cs.sy是eess.sy的别名。本部分包括理论和实验研究，涵盖了自动控制系统的各个方面。本节主要介绍利用建模、仿真和优化工具进行控制系统分析和设计的方法。具体研究领域包括非线性、分布式、自适应、随机和鲁棒控制，以及混合和离散事件系统。应用领域包括汽车和航空航天控制系统、网络控制、生物系统、多智能体和协作控制、机器人学、强化学习、传感器网络、信息物理和能源相关系统的控制以及计算系统的控制。
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Systems and Control        系统与控制
分类描述：This section includes theoretical and experimental research covering all facets of automatic control systems. The section is focused on methods of control system analysis and design using tools of modeling, simulation and optimization. Specific areas of research include nonlinear, distributed, adaptive, stochastic and robust control in addition to hybrid and discrete event systems. Application areas include automotive and aerospace control systems, network control, biological systems, multiagent and cooperative control, robotics, reinforcement learning, sensor networks, control of cyber-physical and energy-related systems, and control of computing systems.
本部分包括理论和实验研究，涵盖了自动控制系统的各个方面。本节主要介绍利用建模、仿真和优化工具进行控制系统分析和设计的方法。具体研究领域包括非线性、分布式、自适应、随机和鲁棒控制，以及混合和离散事件系统。应用领域包括汽车和航空航天控制系统、网络控制、生物系统、多智能体和协作控制、机器人学、强化学习、传感器网络、信息物理和能源相关系统的控制以及计算系统的控制。
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英文摘要：
  We introduce a new setting where a population of agents, each modelled by a finite-state system, are controlled uniformly: the controller applies the same action to every agent. The framework is largely inspired by the control of a biological system, namely a population of yeasts, where the controller may only change the environment common to all cells. We study a synchronisation problem for such populations: no matter how individual agents react to the actions of the controller, the controller aims at driving all agents synchronously to a target state. The agents are naturally represented by a non-deterministic finite state automaton (NFA), the same for every agent, and the whole system is encoded as a 2-player game. The first player (Controller) chooses actions, and the second player (Agents) resolves non-determinism for each agent. The game with m agents is called the m -population game. This gives rise to a parameterized control problem (where control refers to 2 player games), namely the population control problem: can Controller control the m-population game for all m in N whatever Agents does?
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PDF链接：
https://arxiv.org/pdf/1807.00893