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From the abstract: "Traditionally, the tasks associated with game AI revolved around non player character (NPC) behavior at dierent levels of control, varying from navigation and pathnding to decision making. Commercial-standard games developed over the last 15 years and current game productions, however, suggest that the traditional challenges of game AI have been well addressed via the use of sophisticated AI approaches, not necessarily following or inspired by advances in academic practices. The marginal penetration of traditional academic game AI methods in industrial productions has been mainly due to the lack of constructive communication between academia and industry in the early days of academic game AI, and the inability of academic game AI to propose methods that would signicantly advance existing development processes or provide scalable solutions to real world problems. Recently, however, there has been a shift of research focus as the current plethora of AI uses in games is breaking the non-player character AI tradition. A number of those alternative AI uses have already shown a signicant potential for the design of better games. This paper presents four key game AI research areas that are currently reshaping the research roadmap in the game AI eld and evidently put the game AI term under a new perspective. These game AI agship research areas include the computational modeling of player experience, the procedural generation of content, the mining of player data on massive-scale and the alternative AI research foci for enhancing NPC capabilities."