Gaming with Emotions: An Architecture for the Development of Mood-Driven Characters in Video Games

Abstract

In the present dissertation, we study the emotional component of the behavior of artificial characters in video games. Our primary aim is to improve the video game playing experience by increasing the sense of realism in gaming scenarios. For this purpose, we develop an emotion simulation model called EEP that accounts for the impact of external events on a character¿s mood state, and analyze its relevance for the development of mood-driven behaviors as part of the control strategies of artificial characters. In addition, we provide a mechanism that improves the development procedure of video game characters, by developing a new hybrid machine learning model called WEREWoLF that purposefully combines reinforcement learning and evolutionary techniques, so as to automatically generate character control strategies associated to different mood states. Both models are integrated into the AGCBAR architecture, which constitutes the solution proposed in this dissertation to the problem of efficiently designing mood-driven strategies for artificial characters in video games. The AGCBAR architecture is capable of encompassing a broad variety of game engine cores, and is thus applicable to a wide spectrum of video games. We assess the adequacy of the above architecture and its components in different ways. While the EEP model has been evaluated on the basis of the judgment of expert gamers, the WEREWoLF algorithm has undergone a quantitative evaluation in a video game scenario. Finally, we implement the complete architecture together with an experimental video game framework in a complex case study, comparing the development effort of mood-driven artificial characters using the AGCBAR architecture together with EEP and WEREWoLF, to traditional implementation techniques.