Yue Guan

Papers

Total Citations

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About

Yue Guan is a researcher specializing in game theory, multi-agent systems, and adversarial robotics, with a particular focus on strategic resource allocation in dynamic, contested environments. His most notable work, "Dynamic Defender-Attacker Blotto Game" (2022), addresses a fundamental challenge in autonomous systems security: how defender robots can optimally protect networked environments against intelligent adversarial attackers. By formulating this engagement as a discrete-time dynamic game on graph-structured environments, Guan bridges classical Blotto game theory with modern multi-robot coordination, offering rigorous mathematical frameworks for real-world security and defense applications. This work has garnered 22 citations, reflecting growing interest from both the robotics and game theory communities. Guan's research sits at a compelling intersection of theoretical rigor and practical relevance, tackling problems that have direct implications for autonomous surveillance, infrastructure protection, and adversarial AI. His contributions are particularly significant for researchers exploring how teams of autonomous agents can make intelligent, decentralized decisions under adversarial pressure, making his work essential reading for students in robotics, control theory, and multi-agent systems.