iGGi PhD Projects - listing iGGi PhD Projects 2023 Immersive Technology This page displays the supervisor-proposed PhD projects on offer under the above stated theme: If you are interested in any of the projects listed and would like further details and/or to discuss, please email the project supervisor. Please note that you can also frame your own project independently granted that you have secured a supervisor's support. For a list of available supervisors please see the accepting students section of our website. While iGGi has checked that the project descriptions listed below are within iGGi's scope , we wish to highlight that you are still responsible for ensuring that your proposal, too, is in line with this scope, and we would further like to point out that supervisor-framed projects are not prioritised in the application selection process: they are judged by the same criteria as applicant-framed proposals. For guidance to make sure that the proposal you submit (regardless of whether it has been supervisor-framed or created entirely by you) sits within iGGi's scope please refer to this link: https://iggi.org.uk/iggi-scope Navigate to other Themes on offer: Game AI Design & Development Player Research Game Audio Game Data Immersive Technology Creative Computing E-Sports Applied Games Back to ALL Projects Immersive Technology Places That Don't Exist The goal of this project is to combine state-of-the-art 3D computer vision and procedural content generation to create game-ready scene models and assets from existing media. Price Immersive Technology Duration William Smith Read More Immersive Technology Tactile Interaction With Virtual Reality Content In this project the student will explore the use of vibrating motors distributed over the human hand (e.g. using a wearable glove) to give tactile feedback about the physical interactions happening in a VR. Price Immersive Technology Duration Lorenzo Jamone & Valkyrie Industries Read More Load More

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. HandCircus

< Back Amplifying The Uncanny Link Author(s) T Broad, FF Leymarie, M Grierson Abstract More info TBA Link

< Back Beyond playing to win: Diversifying heuristics for GVGAI Link Author(s) C Guerrero-Romero, A Louis, D Perez-Liebana Abstract More info TBA Link

< Back A case study in AI-assisted board game design Link Author(s) J Goodman, A Wallat, D Perez-Liebana, S Lucas Abstract More info TBA Link

< Back Novel Incentives for Phrase Detectives Link Author(s) M Poesio, J Chamberlain, U Kruschwitz, C Madge Abstract More info TBA Link

< Back A local approach to forward model learning: Results on the game of life game Link Author(s) SM Lucas, A Dockhorn, V Volz, C Bamford, RD Gaina, I Bravi, ... Abstract More info TBA Link

< Back How does machine learning affect diversity in evolutionary search? Procedural content generation of video games levels has greatly benefited from machine learning. In such complex domains, generative models can provide representation spaces for evolutionary search. But how expressive are such learned models? How many different levels would they be able to produce? A new paper, co-authored by IGGI PhD researcher Sebastian Berns and Professor Simon Colton, looks at the limitations of generative models in the context of multi-solution optimisation. The work will be presented at the Genetic and Evolutionary Computation Conference (GECCO) and is nominated for a best paper award . The study shows that quality diversity (QD) search in the latent space of a variational auto-encoder yields a solution set of lower diversity than in a manually-defined genetic parameter space. The authors find that learned latent spaces are useful for the comparison of artefacts and recommend their use for distance and similarity estimation. However, whenever a parametric search space is obtainable, it should be preferred over a learned representation space as it produces a higher diversity of solutions. Alexander Hagg, Sebastian Berns, Alexander Asteroth, Simon Colton & Thomas Bäck. (2021). Expressivity of Parameterized and Data-driven Representations in Quality Diversity Search. In Proceedings of the Genetic and Evolutionary Computation Conference. Pre-print available on arXiv https://arxiv.org/abs/2105.04247Accompanying code repository available on Github https://github.com/alexander-hagg/ExpressivityGECCO2021 Previous 27 Jun 2021 Next

< Back Nice is Different than Good: Longitudinal Communicative Effects of Realistic and Cartoon Avatars in Real Mixed Reality Work Meetings Link Author(s) GC Dobre, M Wilczkowiak, M Gillies, X Pan, S Rintel Abstract More info TBA Link

< Back Game AI hyperparameter tuning in rinascimento Link Author(s) I Bravi, V Volz, S Lucas Abstract More info TBA Link

< Back Use of Technology in Brief Interventions Link Author(s) L Gega, MJ Saiger Abstract More info TBA Link

< Back Practical Game Design Tool: State Explorer Link Author(s) R Volkovas, M Fairbank, JR Woodward, S Lucas Abstract More info TBA Link