Multi-Agent Planning with Spatio-Temporal and Topological Constraints using STL-GO
Quick Answer
The paper presents a novel approach for multi-agent planning using Spatio-Temporal Logic with Graph Operators (STL-GO), addressing spatio-temporal and topological constraints.
Quick Take
Two encodings based on Mixed-Integer Programming and Satisfiability Modulo Theory are proposed, evaluated on a multi-UAV search-and-rescue benchmark, demonstrating the expressiveness of the methods under dynamic graph interactions.
Key Points
- Focuses on planning with spatio-temporal and topological constraints.
- Introduces two encodings: Mixed-Integer Programming and Satisfiability Modulo Theory.
- Evaluated on a multi-UAV search-and-rescue benchmark with varying team sizes.
- Provides a unified interface for specifying agent constraints and graph topologies.
- Demonstrates expressiveness under dynamic multi-graph interactions.
DeepSignal Analysis
What happened
The paper introduces a method for multi-agent planning using Spatio-Temporal Logic with Graph Operators (STL-GO). It addresses challenges related to spatio-temporal and topological constraints, proposing two encodings based on Mixed-Integer Programming and Satisfiability Modulo Theory. These methods were evaluated on a multi-UAV search-and-rescue benchmark.
Key evidence
- The authors focus on planning multi-agent paths that adhere to constraints defined in STL-GO, which incorporates spatio-temporal and topological considerations.
- Two encodings are presented: one utilizes mixed-integer programming (MIP) and the other employs satisfiability modulo theory (SMT), both with soundness guarantees.
- The evaluation of these encodings was conducted on a multi-UAV search-and-rescue benchmark, examining variations in team size and graph complexity.
Why it matters
This research is significant as it enhances the capability of multi-agent systems to operate under complex constraints, which is crucial for applications like search-and-rescue missions. By providing a unified interface for specifying agent constraints and graph topologies, the proposed methods may improve efficiency and effectiveness in real-world scenarios. The ability to handle dynamic interactions among agents is particularly relevant in rapidly changing environments.
Paper Resources
📖 Reader Mode
~2 min readAbstract:Multi-agent planning problems arise in a variety of engineering applications, such as multi-robot wildfire fighting and unmanned aerial inspection in factories. A particular challenge is the existence of spatio-temporal (i.e., when and/or where an agent should do what) and topological constraints (i.e., how agents should interact), as typically formalized via the notion of graphs. Over the last years, various frameworks have been proposed that can capture such constraints via spatio-temporal logics. We focus here on spatio-temporal logic with graph operators (STL-GO), a recent formalism that supports reasoning about multiple agents and their topologies, such as sensing, communication, and task topologies. In this paper, we consider the problem of planning multi-agent paths that satisfy constraints written in STL-GO. This problem is particularly challenging due to the need of encoding multiple, potentially time-varying graphs via the graph operators inherent to STL-GO. We present two encodings of this problem, one based on mixed-integer programming (MIP) and another based on satisfiability modulo theory (SMT), with soundness guarantees. We provide a unified interface for specifying agent constraints, their graph topologies, and the STL-GO specification, enabling seamless use of both methods and facilitating direct comparison between them. We evaluate both encodings on a multi-UAV search-and-rescue benchmark, ablating over team size and graph complexity, highlighting the expressiveness of the proposed encodings under dynamic multi- graph interactions.
| Comments: | Accepted at Formal Methods for Computer-Aided Design 2026 |
| Subjects: | Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2607.28679 [cs.AI] |
| (or arXiv:2607.28679v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.28679 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Sheryl Paul [view email]
[v1]
Wed, 29 Jul 2026 16:12:58 UTC (7,777 KB)
— Originally published at arxiv.org
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