Computational techniques for modeling non-player characters in games
Shu Hui Feng · 2014
Modeling of non-player characters (NPCs) is an important research area in the development of computer games.Autonomous NPCs by emulating the behavior of human beings, with realistic performance and believable affective variations, in simulated environment, make the games more challenging and enjoyable.Modeling of NPCs is essentially the problem of creating autonomous agents, which are expected to function and adapt by themselves in a complex environment.The motivation behind this research is thus to create "realistic" and "believable" NPCs with the abilities of autonomy, interactivity, situatedness, learning, and adaptation.Three key problems are considered in this research: (1) how behavior models of NPCs may be learned by mimicking behavior patterns of other players?(2) how behavior models of NPCs may be adapted through interaction and feedback in a dynamic environment?(3) how emotion of NPCs may be modeled and integrated with the behavior system and to create variations of NPCs?For learning behavior models, this research investigates two classes of self-organizing neural networks.Firstly, the self-generating neural network (SGNN) is investigated to learn behavior rules from specific sample bots in a supervised manner.Further optimization of SGNN is also proposed via a pruning method which improves its performance.Our empirical experiments based on a first person shooting game environment called the Unreal Tournament show that SGNN is able to learn behaviors effectively from their prototype.Secondly, another class of self-organizing neural networks, known as Fusion Architecture for Learning, COgnition, and Navigation (FALCON), is adapted for imitative learning to learn behavior patterns in the Unreal Tournament game.Benchmark i 6.6 Samples of emotions expressed by NPCs in response to specific events.6.7 Means and standard deviation of accuracy of CRAA and the other models based on human evaluation. . . . . . . . . . . . . . . . . . . .6.8 Questions used in the human user evaluations. . . . . . . . . . . . . .6.9 Means and standard deviations of human ratings for affective NPCs using CRAA and EMA emotion rules. . . . . . . . . . . . . . . . . .6.10 Emotion appraisal rules used in EMA. . . . . . . . . . . . . . . . . .6.11 Emotion caused by external stimulus which reinforce the behaviors .xiv