arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2104.02737eess.SYcs.AIcs.FLcs.SY
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基于神经网络的多智能体系统时空控制

Neural Network-based Control for Multi-Agent Systems from Spatio-Temporal Specifications

Boston University(波士顿大学);TU Wien(维也纳工业大学)

Suhail Alsalehi, Noushin Mehdipour, Ezio Bartocci, Calin Belta

摘要:

我们提出了一个框架来解决控制综合问题的多智能体网络系统需要满足时空规格。我们使用时空到达和逃逸逻辑(STREL)作为规范语言。对于这种逻辑,我们定义了平滑的定量语义,它捕获了多智能体团队对公式的满意度。我们使用新的定量语义映射与STREL规范的控制综合问题的优化问题,并提出了一个组合的启发式和基于梯度的方法来解决这些问题。由于这种方法可能无法满足实时实现的要求,我们开发了一种机器学习技术,该技术使用离线优化的结果来训练神经网络,该神经网络在当前状态下提供控制输入。我们说明了所提出的框架的有效性,将其应用到一个机器人团队的模型,需要满足通信约束下的时空规范。

英文摘要:

We propose a framework for solving control synthesis problems for multi-agent networked systems required to satisfy spatio-temporal specifications. We use Spatio-Temporal Reach and Escape Logic (STREL) as a specification language. For this logic, we define smooth quantitative semantics, which captures the degree of satisfaction of a formula by a multi-agent team. We use the novel quantitative semantics to map control synthesis problems with STREL specifications to optimization problems and propose a combination of heuristic and gradient-based methods to solve such problems. As this method might not meet the requirements of a real-time implementation, we develop a machine learning technique that uses the results of the off-line optimizations to train a neural network that gives the control inputs at current states. We illustrate the effectiveness of the proposed framework by applying it to a model of a robotic team required to satisfy a spatial-temporal specification under communication constraints.

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。

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