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

• URL: http://arxiv.org/abs/2104.02737v1
• Date: Tue, 6 Apr 2021 18:08:09 GMT
• Title: Neural Network-based Control for Multi-Agent Systems from Spatio-Temporal Specifications
• Authors: Suhail Alsalehi, Noushin Mehdipour, Ezio Bartocci and Calin Belta
• Abstract summary: We use Spatio-Temporal Reach and Escape Logic (STREL) as a specification language. We map control synthesis problems with STREL specifications to propose a combination of gradient and gradient-based methods to solve such problems. 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 current states.
• Score: 0.757024681220677
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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