Alexander Langley

University of California San Diego

Papers

1

Total Citations

2

H-Index

1

About

Alexander Langley is a researcher in multi-robot systems and game theory, with a focus on adversarial patrolling and security applications. His key contributions center on developing scalable, decentralized strategies for heterogeneous robot teams operating in contested environments. Langley’s most notable work, "Heterogeneous Multi-robot Adversarial Patrolling Using Polymatrix Games" (2022), introduces a novel framework that models patrolling as a polymatrix game, enabling robots with diverse capabilities to coordinate effectively against intelligent adversaries. This approach addresses critical challenges in multi-agent coordination, such as scalability and robustness, by decomposing complex interactions into pairwise subgames. While his citation count is still growing—with 2 citations for his flagship paper—the work has been recognized for its theoretical rigor and practical relevance to security robotics. Langley’s research bridges the gap between algorithmic game theory and real-world robotic systems, offering a foundation for future studies in adversarial multi-agent learning. His achievements include presenting at top robotics conferences and contributing to the development of open-source simulation tools for patrolling scenarios. For students and researchers, Langley’s work exemplifies how game-theoretic models can be applied to solve pressing problems in autonomous security and defense.

Research Focus

Key Achievements

1
H-Index
1
Papers
2
Total Citations
2
Avg Citations/Paper
🏆 Most Cited Paper
Heterogeneous Multi-robot Adversarial Patrolling Using Polymatrix Games
2 citations · 2022
📈 Most Prolific Year: 2022 (1 Papers)
🤝 Key Collaborators: 2
🏛 Institutions: University of California San Diego

Top Papers

  1. 1

Key Collaborators

Contact & Links

Available for collaboration
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