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< Back Automated game balancing in Ms PacMan and StarCraft using evolutionary algorithms Link Author(s) M Morosan, R Poli Abstract More info TBA Link

Themes (All) iGGi is a collaboration between Uni of York + Queen Mary Uni of London: the largest training programme worldwide for doing a PhD in digital games. iGGi Themes Game AI How might we use novel AI techniques including machine learning and decision search to create more effective and engaging game agents and understand human behaviour in games? Design & Development How might we advance game-making with new design and development insights, methods, tools, and techniques? Immersive Technology How might we advance the use and understanding of VR, XR, AR, and other immersive technologies in games? Game Audio How might we combine data, AI, and music psychology to create engaging adaptive music and sound experiences for games? Esports How might we use esports data to create immersive audience experiences, help players and teams improve, and understand human performance? Applied Games How might we design and use games to support health, learning, work, and conduct scientific research? Creative Computing How can AI tools enhance the creative process of (human) game creatives? Player Research How might we use and combine diverse methods to understand how people experience games, interact with and through them, and are affected by them? Accessibility How might we design and make games playable and inclusive to as wide a range of people as possible, regardless of background or ability? Game Data How might we analyse the big data exhausts of games to support game developers and understand people inside and outside of games?

< Back The Dark Souls of Archaeology: Recording Elden Ring Link Author(s) F Smith Nicholls, M Cook Abstract More info TBA Link

< Back Searching for an (un) stable equilibrium: experiments in training generative models without data Link Author(s) T Broad, M Grierson Abstract More info TBA Link

< Back Recurrent Neural-Linear Posterior Sampling for Nonstationary Contextual Bandits Link Author(s) A Ramesh, P Rauber, M Conserva, J Schmidhuber Abstract More info TBA Link

< Back Embodied, in-medium design of VR game motion controls using interactive supervised learning Link Author(s) C Gonzalez Diaz Abstract More info TBA Link

< Back A comparison of the effects of haptic and visual feedback on presence in virtual reality Link Author(s) JK Gibbs, M Gillies, X Pan Abstract More info TBA Link

< Back ETHER: Aligning Emergent Communication for Hindsight Experience Replay Link Author(s) Kevin Denamganaï, Daniel Hernandez, Ozan Vardal, Sondess Missaoui, James Alfred Walker Abstract More info TBA Link

< Back Four dilemmas for video game effects scholars: How digital trace data can improve the way we study games Link Author(s) D Zendle, N Ballou, J Cutting, E Petrovskaya Abstract More info TBA Link

< Back Speeding up genetic algorithm-based game balancing using fitness predictors Link Author(s) M Morosan, R Poli Abstract More info TBA Link

iGGi Partners We are excited to be collaborating with a number of industry partners. IGGI works with industry in some of the following ways: Student Industry Knowledge Transfer - this can take many forms, from what looks like a traditional placement, to a short term consultancy, to an ongoing relationship between the student and their industry partner. Student Sponsorship - for some of our students, their relationship with their industry partner is reinforced by sponsorship from the company. This is an excellent demonstration of the strength of the commitment and the success of the collaborations. In Kind Contributions - IGGI industry partners can contribute by attending and/or featuring in our annual conference, offering their time to give talks and masterclasses for our students, or even taking part in our annual game jam! There are many ways for our industry partners to work with IGGI. If you are interested in becoming involved, please do contact us so we can discuss what might be suitable for you. House of Commons

< Back Domain Adaptation for Deep Reinforcement Learning in Visually Distinct Games Link Author(s) DS Ratcliffe, L Citi, S Devlin, U Kruschwitz Abstract More info TBA Link