Real-Time Strategy (RTS) games are popular testbeds for AI researchers. In this paper we compare different machine learning algorithms to predict the outcome of small battles of marines in StarCraft, a popular RTS game. The predictions are made from the perspective of an external observer of the game and they are based only on the actions that the different units perform in the battlefield. Our...

Real-time strategy (RTS) games represent a mainstream genre of video games. They are also practical test-beds for intelligent agents, which have received considerable interest from Artificial Intelligence (AI) researchers, in particular game AI researchers. Terrain knowledge understanding is a fundamental issue for RTS agents and map decomposition methods can help AI agents in representing terr...

Network games are becoming increasingly popular, but their traf ic patterns have received little attention from the academic research community. A better understanding of network game t ra f i c can lead t o more effective network architectures and more realistic network simulations. W e gathered t ra f i c traces from live Internet games of Counter-strike and Starcraft, representative games fr...

Starcraft II is a popular real-time strategy (RTS) game, in which players compete with each other online. Based on their performance, the players are ranked in one of seven leagues. In our research, we aim at constructing a player model that is capable of predicting the league in which a player competes, using observations of their in-game behavior. Based on cognitive research and our knowledge...

Game tree search algorithms, such as Monte Carlo Tree Search (MCTS), require access to a forward model (or “simulator”) of the game at hand. However, in some games such forward model is not readily available. In this paper we address the problem of automatically learning forward models (more specifically, combats models) for two-player attrition games. We report experiments comparing several ap...

Current-generation online games typically incorporate a “computer” opponent to train new players to compete against human opponents. The quality of this training depends to a large degree on how similar the computer’s play is to that of an experienced human player. For instance, inhuman weaknesses in computer play encourage new players to develop tactics, prediction rules and playing styles tha...

Real-Time Strategy (RTS) video games have proven to be a very challenging application area for artificial intelligence research. Existing AI solutions are limited by vast state and action spaces and real-time constraints. Most implementations efficiently tackle various tactical or strategic sub-problems, but there is no single algorithm fast enough to be successfully applied to big problem sets...

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