STL-GO: Spatio-Temporal Logic with Graph Operators for Distributed Systems with Multiple Network Topologies
🤖 AI Summary
Ensuring task objectives, safety, and reliability in multi-agent systems (MASs) remains challenging—particularly under heterogeneous sensing/communication modalities, inter-task dependencies, and coupling via spatial/virtual distances.
Method: This paper proposes STL-GO, a spatiotemporal logic that innovatively integrates graph operators to dynamically quantify local topological structures (e.g., in-degree/out-degree neighborhoods) over directed interaction graphs, while unifying temporal logic for formal modeling. A distributed online monitoring algorithm is designed using only local agent information, eliminating reliance on global state.
Contribution/Results: Evaluated on bike-sharing dispatching and multi-UAV coordination, STL-GO demonstrates significantly enhanced expressiveness and monitoring efficacy for complex spatiotemporal-topological couplings. Compared to existing spatiotemporal logics, it offers superior modeling fidelity and practical deployability in distributed MAS settings.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Modeling multi-agent system requirements with diverse interactions
Monitoring distributed systems using local agent information
Enhancing spatio-temporal logic with graph-based reasoning
Innovation
Methods, ideas, or system contributions that make the work stand out.
Introduces STL-GO logic with graph operators
Models agent interactions via multiple directed graphs
Proposes distributed monitoring using local information
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