Multi-agent learning (MAL) studies how agents learn to behave optimally and adaptively
from their experience when interacting with other agents in dynamic environments. The …

The key challenge in multiagent learning is learning a best response to the behaviour of
other agents, which may be non-stationary: if the other agents adapt their strategy as well …

Policy space response oracles (PSRO) is a promising tool to find an approximate Nash
equilibrium (NE) in a two-player zero-sum game. It solves the equilibrium by iteratively …

Game-theoretic analysis has emerged as an important method for making resource
allocation decisions in both infrastructure protection and cyber security domains. However …

Games have been used as a testbed for artificial intelligence research since the earliest
conceptions of computing itself. The twin goals of defeating human professional players at …

We consider the problem of monitoring a set of targets, using scarce monitoring resources
(eg, sensors) that are subject to adversarial attacks. In particular, we propose a constant …

Normal form games are one of the most familiar representations for modeling interactions
among multiple agent. However, modeling many realistic interactions between agents …

In many real-world continuous action domains, human agents must decide which actions to
attempt and then execute those actions to the best of their ability. However, humans cannot …

The performance of agents in many domains with continuous action spaces depends not
only on their ability to select good actions to execute, but also on their ability to execute …

In domains with continuous action spaces, one characteristic of an agent is their precision in
executing intended actions. An agent's execution skill significantly impacts their success as it …