Development of a model of adaptive behavior of non-player characters based on intelligent agents of neural network

Abstract

The object of research is the process of designing and modeling intelligent agents of a neural network for the behavior of non-player characters (NPCs). Today, the field of video game development continues to progress, but the methodology for creating the "brain" of NPCs still uses primitive approaches with technological limitations. This creates the problem of ludonarrative dissonance, which actualizes this work, that offers a new solution. The research results demonstrate the potential of a new approach to creating intelligent and realistic NPCs in video games. The developed models demonstrate convergence of training, which confirms the ability to understand complex concepts, such as the psychological personality model International Personality Item Pool 50. The conclusions of the work are based on the results of theoretical analysis, technical modeling and practical experimentation. Analysis of the collected data forms the final confirmation of the feasibility of the proposed theory. An increase in the total step reward from –0.00275 to 0.03176 by the median for the best option and from –0.00733 to 0.02734 for the worst was recorded, taking into account the mathematical limit in [–1.0219; 0.9861] and aggressive normalization of the hyperbolic tangent function. The adaptation of models to the environmental conditions was confirmed by reducing the penalty by 30.47% by the median or by 23.59% by the extreme 5% of the data, for the worst configuration. The obtained results can be used in game development, to create the "brain" of NPCs. This will allow to get rid of the exponential complexity of development, inherent in current methodologies due to the proposed approach. Using reinforcement learning technologies, it is possible to make a more variable and detailed virtual context, with lower computational resources consumption, avoiding ludonarrative dissonance.