This paper introduces a multiagent reinforcement learning algorithm that converges with a given accuracy to stationary Nash equilibria in general-sum discounted stochastic games. ...
We consider a resource selection game with incomplete information about the resource-cost functions. All the players know is the set of players, an upper bound on the possible cos...
The objective of this work is to automatically detect the use of game bots in online games based on the trajectories of account users. Online gaming has become one of the most popu...
In spite of its growing popularity, due to a huge technical evolution in the last years and to the fact that new generations are more literate in games than in books, game-based te...
Abstract— Many games require opponent modelling for optimal performance. The implicit learning and adaptive nature of evolutionary computation techniques offer a natural way to d...