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Pre-Conference Talk by PHAM Hoang Giang | Beyond Homogeneous Adversaries: Stackelberg Security Games with Mixed Quantal Response

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Beyond Homogeneous Adversaries: Stackelberg Security Games with Mixed Quantal Response

Speaker:


PHAM Hoang Giang
Ph.D. Candidate
School of Computing and Information Systems
Singapore Management University

 

Date:

Time:

Venue:

 

14 August 2026, Friday

3:00pm – 3:30pm

Meeting room 4.4, Level 4. 
School of Computing and Information Systems 1, 
Singapore Management University, 
80 Stamford Road
Singapore 178902

Please register by 12 August 2026.

About the Talk

The quantal response (QR) model is widely used in Stackelberg security games (SSGs) to capture boundedly rational adversaries. Existing work on SSGs under QR, however, almost exclusively assumes a homogeneous attacker population, ignoring heterogeneity in attacker preferences and rationality. We study SSG with mixed quantal response attackers, where the follower population consists of multiple discrete attacker types, each following a type-specific QR model. The defender allocates limited resources across targets, while an attacker drawn from this heterogeneous population observes the defender's strategy and attacks a single target. This results in a highly non-convex equilibrium computation problem. We develop a polynomial-time approximation scheme for this setting when the number of attacker types is bounded, based on an exponential cone programming formulation combined with a carefully designed Branch-and-Bound procedure. Experiments demonstrate that our approach outperforms standard gradient-based methods and that explicitly modeling attacker heterogeneity yields significant gains over traditional SSG models with a single QR attacker. 

This is a Pre-Conference talk for The 35th International Joint Conference on Artificial Intelligence (IJCAI-ECAI 2026).

About the Speaker

Hoang Giang PHAM is a third-year PhD candidate in Computer Science at SMU School of Computing and Information Systems, supervised by Prof Mai Anh Tien. His research focuses on decision-making and optimization, particularly methods for modeling human choice behavior, with applications spanning transportation planning, revenue management, facility location, and security games.