Team strategy acquisition is one of the most important issues of multiagent systems, especially in an adversary environment. RoboCup has been providing such an environment for AI a...
Agents interacting in a multiagent environment not only have to be wary of interfering with each other when carrying out their tasks, but also should capitalize on opportunities f...
We present a general methodology to automate the search for equilibrium strategies in games derived from computational experimentation. Our approach interleaves empirical game-the...
In many two-sided search applications, autonomous agents can enjoy the advantage of parallel search, powered by their ability to handle an enormous amount of information, in a shor...
We describe a system that successfully transfers value function knowledge across multiple subdomains of realtime strategy games in the context of multiagent reinforcement learning....