Designing efficient algorithms to compute a Nash Equilibrium (NE) in multiplayer games is
still an open challenge. In this paper, we focus on computing an NE that optimizes a given …

Containment of dynamic crimes such as kidnapping, bank robbery, hit-and-run, etc., is a
challenging issue for law enforcement agencies as the criminal changes his location with …

Deep reinforcement learning is an agent modeling method with both deep learning feature
extraction ability and reinforcement learning sequence decision-making ability, which can …

Most violent crimes happen in urban and suburban cities. With emerging tracking
techniques, law enforcement officers can have real-time location information of the escaping …

Securing networked infrastructures is important in the real world. The problem of deploying
security resources to protect against an attacker in networked domains can be modeled as …

Pursuit-evasion games (PEGs) model interactions between a team of pursuers and an
evader in graph-based environments such as urban street networks. Recent advancements …

Advanced persistent threat (APT) for data theft poses a severe threat to cloud storage
systems (CSSs). An APT actor may steal valuable data from the target CSS even in a …

The escape interdiction problem within the context of attacker activities on a transportation
network is addressed in this study. In the absence of traffic within the network, the attacker …

In many real-world scenarios, a team of agents coordinate with each other to compete
against an opponent. The challenge of solving this type of game is that the team's joint …

How resources are deployed to secure critical targets in networks can be modelled by
Network Security Games (NSGs). While recent advances in deep learning (DL) provide a …