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Many modern games provide environments in which agents perform decision making at several levels of granularity. In the domain of real-time strategy games, an effective agent must make high-level strategic decisions while simultaneously controlling individual units in battle. We advocate reactive planning as a powerful technique for building multi-scale game AI and demonstrate that it enables the...
This paper presents a search-based method for generating maps for the popular real-time strategy (RTS) game StarCraft. We devise a representation of StarCraft maps suitable for evolutionary search, along with a set of fitness functions based on predicted entertainment value of those maps, as derived from theories of player experience. A multiobjective evolutionary algorithm is then used to evolve...
Controlling a virtual camera in 3D computer games is a complex task. The camera is required to react to dynamically changing environments and produce high quality visual results and smooth animations. This paper proposes an approach that combines local and global search to solve the virtual camera control problem. The automatic camera control problem is described and it is decomposed into sub-problems;...
Pac-Man was developed in the 1980s, becoming one of the most popular arcade games of its time. It still has a significant following today and has recently attracted the attention of artificial intelligence researchers, in part, due to the fact that the agent must react in real time in order to navigate its way through the maze. This paper forms an entry to the 2010 IEEE Conference on Computational...
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