TY - JOUR
T1 - Distributed Policy Synthesis of Multiagent Systems with Graph Temporal Logic Specifications
AU - Cubuktepe, Murat
AU - Xu, Zhe
AU - Topcu, Ufuk
N1 - Funding Information:
This work was supported in part by the Office of Naval Research under Grant N00014-19-1-2054 and in part by the National Science Foundation under Grants 1646522 and 1652113. This article was presented in part at International Conference on Autonomous Agents and Multiagent Systems, Auckland, New Zealand, 2020 [16].
Publisher Copyright:
© 2014 IEEE.
PY - 2021/12/1
Y1 - 2021/12/1
N2 - We study the distributed synthesis of policies for multiagent systems to perform spatial-temporal tasks. We formalize the synthesis problem as a factored Markov decision process subject to graph temporal logic specifications. The transition function and the task of each agent are functions of the agent itself and its neighboring agents. In this work, we develop another distributed synthesis method, which improves the scalability and runtime by two orders of magnitude compared to our prior work. The synthesis method decomposes the problem into a set of smaller problems, one for each agent by leveraging the structure in the model, and the specifications. We show that the running time of the method is linear in the number of agents. The size of the problem for each agent is exponential only in the number of neighboring agents, which is typically much smaller than the number of agents. We demonstrate the applicability of the method in case studies on disease control, urban security, and search and rescue. The numerical examples show that the method scales to hundreds of agents with hundreds of states per agent and can also handle significantly larger state spaces than our prior work.
AB - We study the distributed synthesis of policies for multiagent systems to perform spatial-temporal tasks. We formalize the synthesis problem as a factored Markov decision process subject to graph temporal logic specifications. The transition function and the task of each agent are functions of the agent itself and its neighboring agents. In this work, we develop another distributed synthesis method, which improves the scalability and runtime by two orders of magnitude compared to our prior work. The synthesis method decomposes the problem into a set of smaller problems, one for each agent by leveraging the structure in the model, and the specifications. We show that the running time of the method is linear in the number of agents. The size of the problem for each agent is exponential only in the number of neighboring agents, which is typically much smaller than the number of agents. We demonstrate the applicability of the method in case studies on disease control, urban security, and search and rescue. The numerical examples show that the method scales to hundreds of agents with hundreds of states per agent and can also handle significantly larger state spaces than our prior work.
KW - Cyber-physical systems
KW - multi-agent systems
KW - networked control systems
KW - optimization
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U2 - 10.1109/TCNS.2021.3084553
DO - 10.1109/TCNS.2021.3084553
M3 - Article
AN - SCOPUS:85107230544
VL - 8
SP - 1799
EP - 1810
JO - IEEE Transactions on Control of Network Systems
JF - IEEE Transactions on Control of Network Systems
SN - 2325-5870
IS - 4
ER -