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< Back Increasing the Diversity of Deep Generative Models Link Author(s) S Berns Abstract More info TBA Link

< Back Generating Diverse and Competitive Play-Styles for Strategy Games Link Author(s) D Perez-Liebana, C Guerrero-Romero, A Dockhorn, L Xu, J Hurtado, Dominik Jeurissen Abstract More info TBA Link

< Back Delivering Bad News: VR Embodiment of Self Evaluation in Medical Communication Training Link Author(s) T Collingwoode-Williams, M Gillies, P Nambyiah, C Fertleman, X Pan Abstract More info TBA Link

< Back How Players Learn Team-versus-Team Esports: First Results from A Grounded Theory Study Link Author(s) J Hesketh, CS Deterding, J Gow Abstract More info TBA Link

< Back How do video games affect mental health? A narrative review of 13 proposed mechanisms Link Author(s) N Ballou, T Hakman, M Vuorre, K Magnusson, AK Przybylski Abstract More info TBA Link

< Back Experience as a transformational practice Link Author(s) C Ramirez-Figueroa, GL Amadei, N Binyamini Ben Meir, C Martin, ... Abstract More info TBA Link

< Back Dr Martin Balla Queen Mary University of London iGGi Alum Available for post-PhD position Before starting his PhD Martin studied Computer Science at the University of Essex. His main interest is artificial intelligence and its application to all sort of problems ranging from computer vision to game AI. He likes spending his spare time with various activities which mainly involves reading, playing video games and skateboarding. Martin's PhD thesis focuses on Reinforcement Learning agents that can adapt to changes in the reward function and/or changes in the environment. His work investigates how agents can transfer their knowledge to changes in the environment, such as new rewards, levels or visuals. Outside of his main research direction, Martin is involved with the Tabletop games framework (TAG), which is a collection of various tabletop games implemented with a common API with a focus on various game-playing agents (including RL). TAG brings various challenges to RL agents compared to search-based agents, such as complex action spaces, unique observation spaces (various embeddings), multi-agent dynamics with competitive and collaborative aspects, and lots of hidden information and stochasticity. m.balla@qmul.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/martinballa LinkedIn BlueSky https://martinballa.github.io Github Supervisors: Dr Diego Pérez-Liébana Prof. Simon Lucas Featured Publication(s): PyTAG: Tabletop Games for Multi-Agent Reinforcement Learning PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games Illuminating Game Space Using MAP-Elites for Assisting Video Game Design PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games TAG: Pandemic Competition Task Relabelling for Multi-task Transfer using Successor Features TAG: A tabletop games framework Design and implementation of TAG: a tabletop games framework Evaluating generalisation in general video game playing Evaluating Generalization in General Video Game Playing Analysis of statistical forward planning methods in Pommerman Themes Game AI - Previous Next

< 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 UK Games Expo Birmingham With its focus (almost entirely) on board games, the UK Games Expo wasn't exactly an obvious choice for running an iGGi stand. But due to the fact that a considerable proportion of iGGi PG Researchers has involvement with tabletop games through their research in some form or other, the suggestion had been made for a few years now, and finally, iGGi decided to test the waters. As an added encouragement, Tabletop R&D - an SME that sprung off from our very own Game AI Group at QMUL - had offered to share a stand space with us. We (i.e., the 8 iGGis who attended) were positively surprised on more than one level, namely by the size of the expo (4 large, hangar-like exhibition halls) the welcoming and friendly vibes from the community (and the fact that it felt like a community gathering rather than a trade fair) the quality of conversations we had at the stand, which also made our presence feel relevant the new connections we forged just the charm of the whole thing We're definitely all rooting for coming back next year! Previous 3 Jun 2025 Next

< Back Planning in GVGAI Link Author(s) DP Liebana, RD Gaina Abstract More info TBA Link

< Back From social media to artificial intelligence: improving research on digital harms in youth Link Author(s) KL Mansfield, S Ghai, T Hakman, N Ballou, M Vuorre, AK Przybylski 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. Creative AI