Opponent modeling is a key challenge in Real-Time Strategy (RTS) games as the environment is adversarial in these games, and the player cannot predict the future actions of her opponent. Additionally, the environment is partially observable due to the fog of war. In this paper, we propose an opponent model which is robust to the observation noise existing due to the fog of war. In order to cope...

Real-time strategy games present an environment in which game AI is expected to behave realistically. One feature of realistic behaviour in game AI is the ability to recognise the strategy of the opponent player. This is known as opponent modeling. In this paper, we propose an approach of opponent modeling based on hierarchically structured models. The top-level of the hierarchy can classify th...

In real-time strategy games players make decisions and control their units simultaneously. Players are required to make decisions under time pressure and should be able to control multiple units at once in order to be successful. We present the design and implementation of a multi-agent interface for the real-time strategy game STARCRAFT: BROOD WAR. This makes it possible to build agents that c...

We consider the problem of effective and automated decisionmaking in modern real-time strategy (RTS) games through the use of reinforcement learning techniques. RTS games constitute environments with large, high-dimensional and continuous state and action spaces with temporally-extended actions. To operate under such environments we propose Exlos, a stable, model-based MonteCarlo method. Contra...