Modern films, games and virtual reality applications are dependent on convincing computer graphics. Highly complex models are a requirement for the successful delivery of many scenes and environments. While workflows such as rendering, compositing and animation have been streamlined to accommodate increasing demands, modelling complex models is still a laborious task. This paper introduces the computational benefits of an Interactive Genetic Algorithm (IGA) to computer graphics modelling while compensating the effects of user fatigue, a common issue with Interactive Evolutionary Computation. An intelligent agent is used in conjunction with an IGA that offers the potential to reduce the effects of user fatigue by learning from the choices made by the human designer and directing the search accordingly. This workflow accelerates the layout and distribution of basic elements to form complex models. It captures the designer’s intent through interaction, and encourages playful discovery
This paper describes a study that examines the impact that procedurally generated content has on the quality of gaming experience. To that end, an experimental study has been undertaken where gamers play two versions of an otherwise identical game, the only difference being that in one version the game levels are designed by a human designer and in the second version they are procedurally generated. A game immersion questionnaire is used to capture the quality of the gameplay experience and the results across the two groups compared. Whilst there are observable differences in perceived total immersion, statistical analysis using one way ANOVA testing suggests that the difference is not statistically significant. Detailed analysis of the questionnaire responses identifies where variation between the two groups is statistically significant.
This paper presents a novel approach to procedural generation of urban maps for First Person Shooter (FPS) games. A multi-agent evolutionary system is employed to place streets, buildings and other items inside the Unity3D game engine, resulting in playable video game levels. A computational agent is trained using machine learning techniques to capture the intent of the game designer as part of the multi-agent system, and to enable a semi-automated aesthetic selection for the underlying genetic algorithm.
CCS Concepts• Software and its engineering➝Software organization and properties➝ Contextual software domains➝Virtual worlds software➝Interactive games• Applied computing➝Computers in other domains➝ Personal computers and PC applications➝ Computer games
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