STL-GO: Spatio-Temporal Logic with Graph Operators for Distributed Systems with Multiple Network Topologies

Multi-agent systems (MASs) consisting of a number of autonomous agents that communicate, coordinate, and jointly sense the environment to achieve complex missions can be found in a variety of applications such as robotics, smart cities, and internet-of-things applications. Modeling and monitoring MAS requirements to guarantee overall mission objectives, safety, and reliability is an important problem. Such requirements implicitly require reasoning about diverse sensing and communication modalities between agents, analysis of the dependencies between agent tasks, and the spatial or virtual distance between agents. To capture such rich MAS requirements, we model agent interactions via multiple directed graphs, and introduce a new logic – Spatio-Temporal Logic with Graph Operators (STL-GO). The key innovation in STL-GO are graph operators that enable us to reason about the number of agents along either the incoming or outgoing edges of the underlying interaction graph that satisfy a given property of interest; for example, the requirement that an agent should sense at least two neighboring agents whose task graphs indicate the ability to collaborate. We then propose novel distributed monitoring conditions for individual agents that use only local information to determine whether or not an STL-GO specification is satisfied. We compare the expressivity of STL-GO against existing spatio-temporal logic formalisms, and demonstrate the utility of STL-GO and our distributed monitors in a bike-sharing and a multi-drone case study.

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Publication Details

Journal
ACM Transactions on Embedded Computing Systems
Published
2025-08-11
DOI
https://doi.org/10.1145/3760258
Primary Topic
Logic, Reasoning, and Knowledge
Type
article
Field-Weighted Citation Impact
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article

STL-GO: Spatio-Temporal Logic with Graph Operators for Distributed Systems with Multiple Network Topologies

Lars Lindemann, Yiqi Zhao, Bardh Hoxha, Jyotirmoy V. Deshmukh et al.
ACM Transactions on Embedded Computing Systems
Logic, Reasoning, and Knowledge
article

STL-GO: Spatio-Temporal Logic with Graph Operators for Distributed Systems with Multiple Network Topologies

Lars Lindemann, Yiqi Zhao, Bardh Hoxha, Jyotirmoy V. Deshmukh, Georgios Fainekos, Xiao Jin Yu
article en

Abstract

Multi-agent systems (MASs) consisting of a number of autonomous agents that communicate, coordinate, and jointly sense the environment to achieve complex missions can be found in a variety of applications such as robotics, smart cities, and internet-of-things applications. Modeling and monitoring MAS requirements to guarantee overall mission objectives, safety, and reliability is an important problem. Such requirements implicitly require reasoning about diverse sensing and communication modalities between agents, analysis of the dependencies between agent tasks, and the spatial or virtual distance between agents. To capture such rich MAS requirements, we model agent interactions via multiple directed graphs, and introduce a new logic – Spatio-Temporal Logic with Graph Operators (STL-GO). The key innovation in STL-GO are graph operators that enable us to reason about the number of agents along either the incoming or outgoing edges of the underlying interaction graph that satisfy a given property of interest; for example, the requirement that an agent should sense at least two neighboring agents whose task graphs indicate the ability to collaborate. We then propose novel distributed monitoring conditions for individual agents that use only local information to determine whether or not an STL-GO specification is satisfied. We compare the expressivity of STL-GO against existing spatio-temporal logic formalisms, and demonstrate the utility of STL-GO and our distributed monitors in a bike-sharing and a multi-drone case study.

ACM Transactions on Embedded Computing SystemsVol. 24(5s)
University of Southern California (US), Toyota Motor Corporation (Switzerland) (CH), Toyota Research Institute
Industry, innovation and infrastructure
Openalex Percentile: Top 89%
Logic, Reasoning, and Knowledge
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