Artificial intelligence for social good (AI4SG) is a research theme that aims to use and
advance artificial intelligence to address societal issues and improve the well-being of the …

Abstract Green Security Games (GSGs) have been proposed and applied to optimize patrols
conducted by law enforcement agencies in green security domains such as combating …

Designing intelligent agents that interact proficiently with people necessitates the prediction
of human decision-making. We present and discuss three prediction paradigms for …

A wealth of algorithms centered around (integer) linear programming have been proposed
to compute equilibrium strategies in security games with discrete states and actions …

Traditional security games concern the optimal randomized allocation of human patrollers,
who can directly catch attackers or interdict attacks. Motivated by the emerging application of …

Stackelberg equilibria have become increasingly important as a solution concept in
computational game theory, largely inspired by practical problems such as security settings …

In this paper, we study Censored Semi-Bandits, a novel variant of the semi-bandits problem.
The learner is assumed to have a fixed amount of resources, which it allocates to the arms at …

Finding Nash equilibrium in continuous action spaces is a challenging problem and has
applications in domains such as protecting geographic areas from potential attackers. We …

Drones can assist in mitigating traffic accidents by deterring reckless drivers, leveraging their
flexible mobility. In the real-world, drones are fundamentally limited by their battery/fuel …

Stochastic policy gradient methods using neural representations have had considerable
success in single-agent domains with continuous action spaces. These methods typically …