arXiv:2501.17559v2 Announce Type: replace Abstract: After the achievement of solving two-player zero-sum games, more AI researchers focus on solving multiplayer games. Urban Network Security Games (\textbf{UNSGs}) represent a class of such games, modeling real-world scenarios where law enforcement must strategically allocate limited resources to intercept criminals escaping within urban networks, and have gained considerable research attention. However, progress in this field has been limited by the absence of a standardized experimental platform and realistic benchmarks with heterogeneous tra
The proliferation of complex multi-agent systems and the increasing sophistication of AI in game theory necessitate robust benchmarks for evaluating strategic AI capabilities in urban security contexts.
This development addresses a critical gap in standardized platforms for Urban Network Security Games, which are relevant to real-world strategic resource allocation for law enforcement and defense.
The introduction of GraphChase provides a standardized experimental platform and realistic benchmarks, enabling more rigorous and comparable research into multi-player AI strategy for urban security.
- · AI researchers in game theory
- · Law enforcement agencies
- · Defence tech developers
- · Criminal organizations
Improved AI models for strategic resource deployment in urban environments.
Enhanced capabilities for threat interdiction and mitigation in smart cities.
The application of advanced multi-agent AI to other complex strategic human-in-the-loop systems.
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Read at arXiv cs.AI