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

Autor:	Alsalehi, Suhail, Mehdipour, Noushin, Bartocci, Ezio, Belta, Calin
Rok vydání:	2021
Předmět:	Electrical Engineering and Systems Science - Systems and Control Computer Science - Artificial Intelligence Computer Science - Formal Languages and Automata Theory
Druh dokumentu:	Working Paper
DOI:	10.1109/CDC45484.2021.9682921
Popis:	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. Comment: 8 pages. Submitted to the CDC 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2104.02737 Zobrazit plný text záznamu View this record from Arxiv