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Towards autonomous behavior learning of non-player characters in games
2016
Expert systems with applications
Non-Player-Characters (NPCs), as found in computer games, can be modelled as intelligent systems, which serve to improve the interactivity and playability of the games. Although reinforcement learning (RL) has been a promising approach to creating the behaviour models of non-player characters (NPC), an initial stage of exploration and low performance is typically required. On the other hand, imitative learning (IL) is an effective approach to pre-building a NPC's behavior model by observing the
doi:10.1016/j.eswa.2016.02.043
fatcat:vfsatkxgdfcv7is3gb4kgatydm