< Back Streaming Behaviour: Livestreaming as a Paradigm for Analysis of Emotional and Social Signals Link Author(s) C Ringer, MA Nicolaou 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. Rebellion

< Back A Survey on AI and Ethics: Key factors in building AI trust and awareness across European citizens. Link Author(s) Cristian Barrué, Atia Cortés, Alessandro Fabris, Francesca Foffano, Long Pham, Teresa Scantamburlo Abstract More info TBA Link

< Back Interactive Machine Learning for Generative Models Link Author(s) Junichi Shimizu, Ireti Olowe, Terence Broad, Gabriel Vigliensoni, Prashanth Thattai Ravikumar, Rebecca Fiebrink 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. BiG

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. Safe In Our World

< Back Comparative evaluation in the wild: Systems for the expressive rendering of music Link Author(s) K Worrall, Z Yin, T Collins Abstract More info TBA Link

< Back 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? Project areas include: AI agents that play games using Deep Reinforcement Learning and Statistical forward planning AI agents as non-player characters General Video Game AI - AI that can be transferred between video games AI agents for game balancing and Quality Assurance testing << Previous Theme page Next Theme page >> iGGi >>> People <<< relevant to this Theme: Michael Aichmüller iGGi Alum Game AI, Applied Games Read More Dr Martin Balla iGGi Alum Game AI Read More Dr Daniel Berio iGGi Alum Game AI Read More Dr Ignacio Castro Supervisor Game AI, Applied Games, Player Research, Game Data Read More Dr Memo Akten iGGi Alum Game AI Read More Prof. Richard Bartle Supervisor Design & Development, Game AI, Player Research Read More Toby Best iGGi PG Researcher Available for post-PhD position Game AI, Design & Development, Player Research Read More Dr Tom Cole iGGi Alum + Supervisor Game AI, Design & Development Read More Dr Myat Aung iGGi Alum Game AI Read More Matt Bedder iGGi Alum Game AI Read More Dr Ivan Bravi iGGi Alum Game AI, Player Research Read More Dr Tom Collins Supervisor Game AI, Game Audio, Game Data, Player Research, Esports Read More Load More iGGi People working in this Theme iGGi >>> Publications <<< relevant to this Theme: Tiny Moves: Game-based Hypothesis Refinement A Dobrowolska, R Hintzen, M Balla, K Gemayel, S Reichert, T Charman, ... arXiv preprint arXiv:2602.09801, 2026 Dr Martin Balla View Details Gaze for Dyads & Blinks for Triads: Task Engagement Predictors in Free-Flow VR Interactions C Gonzalez Diaz, GC Dobre, E Andre, C Conati 2026 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR) Dr Cristina Dobre View Details Beyond the Spotlight: Co-Designing AI for Theatre Audience Communication A Pedrassoli Chitayat, S Carter, BA Robertson, J Hook Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems Dr Alan Pedrassoli Chitayat Steph Carter View Details Language Model Inversion through End-to-End Differentiation KY Denamganaï, K Subr arXiv preprint arXiv:2602.11044, 2026 Dr Kevin Denamganaï View Details Statistical Forward Planning Algorithms SM Lucas, J Goodman, M Tot Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2025 Dr Marko Tot View Details World and human action models towards gameplay ideation A Kanervisto, D Bignell, LY Wen, M Grayson, R Georgescu, ... Nature 638 (8051), 656-663, 2025 Dr Marko Tot View Details Load More iGGi Publications for this Theme Previous Next

< Back iGGi at IEEE CoG - Madrid A whooping nine iGGi PG Researchers had their paper/short paper/demo accepted and gave a talk at last week’s IEEE Conference on Games (CoG) ! That’s on top of the many iGGi Alumni who featured in the programme through their companies or with their own work. iGGi also ran its own stand by the main auditorium entrance. Here are some key impressions from our time at IEEE CoG in Madrid last week. First up, reasons for celebration: iGGi PG Researcher Filip Sroka won the runner up Best Paper Award on his paper titled “Segment-Based Dynamic Difficulty Adjustment for Motor Learning in VR Rhythm Games”, which explores the use of dynamic difficulty adjustment and procedural content generation to support motor learning in VR rhythm games - big congratulations to Filip and co-author Laurissa Tokarchuk ! iGGi Alum James Goodman won the Best Reviewer Award - well deserved, big congrats to him! The award was called into existence this year to give recognition to those following best practice in peer review. iGGi PG Researcher Toby Best won the Best Demo Award with his demo titled “‘Descent: Journeys in the Dark' Computer-Aided Content Generation” Our honorable mentions go to (1) iGGi Alum Kyle Worrall who had been nominated for “Best of TOG” (IEEE Transactions on Games journal) with his paper titled “Considerations and Concerns of Professional Game Composers Regarding Artificially Intelligent Music Technology” and (2) to iGGi Alum Laura Helsby who had been nominated for Best Reviewer. Very commendable achievements, both! This year's conference: The conference audience seemed to have increased in number compared to last year. The venue was the Information Science building of the Universidad Complutense which has a spacious and (considering the 37°C outside) surprisingly airy/cool interior; the event committee and organisers had gone out of their way to make participants feel welcome and well looked after. A particularly big thanks goes to General Chairs Raluca Gaina and Pedro Antonio González Calero , to Program Chair Diego Pérez Liébana , Local Chairs Irene Camps Ortueta and Pablo Gutiérrez Sánchez , and Aura Dominguez , and the fabulous team at large. iGGi Presence: Our credits also go to all the iGGi PG Researchers not yet mentioned above who, each, delivered stellar talks and presented their work so comprehensively: Dien Nguyen , Dominik Jeurissen , George Long , Joshua Kritz , Lauren Winter , Ruizhe "Jay" Yu Xia , Tamsin Isaac . And lastly, shoutout to the "iGGi-wing of SIE", Adrián Barahona-Ríos and Ryan Spick , as well as to our now officially iGGi-certified Madrid tour guide Carlos González Díaz - it was nice to be able to catch up with you all, and we're already looking forward to seeing you again at BiGGi Con next week! We’re definitely hoping to be back to CoG again next year! Previous 7 Sept 2026 Next

Places That Don't Exist Theme Immersive Technology Project proposed & supervised by William Smith To discuss whether this project could become your PhD proposal please email: william.smith@york.ac.uk < Back Places That Don't Exist Project proposal abstract: Imagine playing a video game inside your favourite movie, with scenes from the movie exactly recreated in all their detail. Or playing a game at a historical site, building or city that has since been destroyed, with photorealistic appearance as it would have appeared. 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 movies, contemporary photos, plans or works of art. 3D reconstruction techniques such as structure-from-motion or deep monocular depth estimation can be used to reconstruct raw models of the observed part of the scene. Deep learning based methods will then be used to extrapolate and clean the models to produce complete scene layouts with photoreal textures. Sample References: https://github.com/skanti/scenecad https://github.com/nianticlabs/monodepth2 Supervisor: William Smith Based at:

< Back Visualizing Multiplayer Game Spaces Link Author(s) J Goodman, D Perez-Liebana, S Lucas Abstract More info TBA Link

< Back Enabled players: The value of accessible digital games Link Author(s) P Cairns, C Power, M Barlet, G Haynes, C Kaufman, J Beeston Abstract More info TBA Link