• Activity Recognition Evaluation Via Machine Learning

      Rameka, ANA; Connor, AM; Kruse, J (European Alliance for Innovation (EAI), 2019)
      With the proliferation of relatively cheap Internet of Things (IoT) devices, smart environments have been highlighted as an example of how the IoT can make our lives easier. Each of these ‘things’ produces data which can ...
    • Evaluating the impact of procedurally generated content on game immersion

      Connor, AM; Greig, TJ; Kruse, J (Springer, 2017)
      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 ...
    • Evolutionary Generation of Game Levels

      Connor, AM; Greig, TJ; Kruse, J (Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (ICST), 2018)
      This paper outlines an approach for evolutionary procedural generation of video game content. The study deals with the automatic generation of game level designs using genetic algorithms and the development of a fitness ...
    • Multi-agent evolutionary systems for the generation of complex virtual worlds

      Kruse, J; Connor, AM (European Alliance for Innovation (EAI), 2015)
      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 ...
    • Procedural urban environments for FPS Games

      Kruse, J; Sosa, R; Connor, AM (Association for Computing Machinery (ACM), 2016)
      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 ...