Solving Urban Network Security Games: Learning Platform, Benchmark, and Challenge for AI Research
Shuxin Zhuang, Shuxin Li, Tianji Yang, Muheng Li, Xianjie Shi, Bo An,, Youzhi Zhang

TL;DR
This paper introduces GraphChase, an open-source platform for modeling, developing, and benchmarking algorithms for Urban Network Security Games, which simulate real-world police-criminal interactions in city environments, addressing scalability and cooperation challenges.
Contribution
The paper presents a flexible, unified platform for UNSGs that supports various game variants, enabling efficient algorithm development and benchmarking for complex multiplayer security scenarios.
Findings
Provides a scalable, open-source platform for UNSGs
Supports multiple game variants with real-world relevance
Facilitates development of advanced algorithms for multiplayer games
Abstract
After the great achievement of solving two-player zero-sum games, more and more AI researchers focus on solving multiplayer games. To facilitate the development of designing efficient learning algorithms for solving multiplayer games, we propose a multiplayer game platform for solving Urban Network Security Games (\textbf{UNSG}) that model real-world scenarios. That is, preventing criminal activity is a highly significant responsibility assigned to police officers in cities, and police officers have to allocate their limited security resources to interdict the escaping criminal when a crime takes place in a city. This interaction between multiple police officers and the escaping criminal can be modeled as a UNSG. The variants of UNSGs can model different real-world settings, e.g., whether real-time information is available or not, and whether police officers can communicate or not. The…
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Taxonomy
TopicsNetwork Security and Intrusion Detection · Information and Cyber Security
MethodsFocus
