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< Back Action Selection in the Creative Systems Framework Link Author(s) S Linkola, C Guckelsberger, A Kantosalo Abstract More info TBA Link

< Back iGGi Con 2023 - Get Ready! Preparations for the next iGGi conference are underway! Better mark the date: iGGi Con 2023 13. + 14. September 2023 Queen Mary University of London This year's event will be packed with talks, workshops, panels, posters and more. For the first time ever, we will also run a Mini Expo with Industry stands. iGGi Con 2023 is a showcase for iGGi PGRs and friends, and iGGi Industry Partners as well as a networking platform for everyone interested/involved in the games industry and games research. So, don't miss out and REGISTER HERE TODAY !! Spaces are limited. Previous 3 Apr 2023 Next

iGGi PhD Projects - listing iGGi PhD Projects 2023 Game Data 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 Game Data Building Player Profiles in Mobile Monetisation: A Machine Learning Approach This project aims to use machine learning techniques to segment and profile mobile gamers in terms of their in-game spending. Price Game Data Duration David Zendle Read More Game Data Understanding ongoing mental states using video games: applications to mental health research. This project will use a combination of neuroscience and advanced data analysis methods to examine the link between video game play and the brain. We will use a combination of cutting-edge data analytic techniques applied to large, existing video game telemetry datasets and neuroimaging experiments designed to measure changes in ongoing mental states while people play simple video games. Price Game Data Duration Alex Wade Read More Load More

< 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 Available for post-PhD position Game AI Read More Matt Bedder iGGi Alum Game AI Read More Toby Best iGGi PG Researcher Available for placement Game AI, Design & Development, Player Research Read More Dr Memo Akten iGGi Alum Game AI Read More Dr Mathieu Barthet Supervisor Game AI, Game Audio Read More Prof. David Beer Supervisor Player Research, Applied Games, Creative Computing, Game Data, Game AI Read More Dr Adrian Bors Supervisor Game AI Read More Dr Myat Aung iGGi Alum Game AI Read More Prof. Richard Bartle Supervisor Design & Development, Game AI, Player Research Read More Dr Daniel Berio iGGi Alum Game AI Read More Dr Ivan Bravi iGGi Alum Game AI, Player Research Read More Load More iGGi People working in this Theme iGGi >>> Publications <<< relevant to this Theme: World and human action models towards gameplay ideation A Kanervisto, D Bignell, LY Wen, M Grayson, R Georgescu, ... Nature 638 (8051), 656-663, 2025 Marko Tot View Details Towards an Ontology of Wargame Design L Ouriques, CE Barbosa, J Kritz, G Xexéo IEEE Access, vol. 13 Joshua Kritz View Details When 1+ 1 does not equal 2: Synergy in games Joshua Kritz, Raluca Gaina arXiv preprint arXiv:2502.10304 Joshua Kritz View Details Archaeological Gameworld Affordances: A Grounded Theory of How Players Interpret Environmental Storytelling F Smith Nicholls, M Cook CHI '25: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, Yokohama, Japan, Article no 465, p. 1 - 20 Florence Smith Nicholls View Details Seeding for Success: Skill and Stochasticity in Tabletop Games J Goodman, D Perez-Liebana, S Lucas IEEE Transactions on Games, 2025 Dr James Goodman View Details Efficient solutions for an intriguing failure of llms: Long context window does not mean LLMs can analyze long sequences flawlessly Peyman Hosseini, Ignacio Castro, Iacopo Ghinassi, Matthew Purver 31st International Conference on Computational Linguistics (COLING) Peyman Hosseini View Details Load More iGGi Publications for this Theme Previous Next

< Back GAIG Meetup The recent Game AI Meetup took place on 01 March 2023. Talks and presentation included: Jakob Foerster (University of Oxford, UK): Opponent-Shaping and Interference in General-Sum Games Original talk abstract: In general-sum games, the interaction of self-interested learning agents commonly leads to collectively worst-case outcomes, such as defect-defect in the iterated prisoner's dilemma (IPD). To overcome this, some methods, such as Learning with Opponent-Learning Awareness (LOLA), shape their opponents' learning process. However, these methods are myopic since only a small number of steps can be anticipated, are asymmetric since they treat other agents as naive learners, and require the use of higher-order derivatives, which are calculated through white-box access to an opponent's differentiable learning algorithm. In this talk I will first introduce Model-Free Opponent Shaping (M-FOS), which overcomes all of these limitations. M-FOS learns in a meta-game in which each meta-step is an episode of the underlying (``inner'') game. The meta-state consists of the inner policies, and the meta-policy produces a new inner policy to be used in the next episode. M-FOS then uses generic model-free optimisation methods to learn meta-policies that accomplish long-horizon opponent shaping. I will finish off the talk with our recent results for adversarial (or cooperative) cheap-talk: How can agents interfere with (or support) the learning process of other agents without being able to act in the environment? Vanessa Volz ( modl.ai ): Establishing Trust in AI-based Tools for Game Development Original talk abstract: AI-based tools to support the game development process have long been a topic in Game AI research, with popular publications in testing, churn prediction, asset, level and even game generation. However, the adaptation of these techniques from the games industry has been hesitant at best: The small-scale and simplified examples researchers use to demonstrate their work understandably only seldom convince the industry to risk investing in AI tools. In this talk, I will speak about my experience establishing trust in AI-based tools to support creative processes in game development. Having worked on this topic in both industry and academia, I will address issues ranging from establishing a common language and explaining AI behaviour to issuing performance guarantees via benchmarking and theoretical analysis. Mike Preuss (Leiden University, The Netherlands): In the eye of the storm? Where are we going with game AI? Original talk abstract: Looking back at the last 10 years of research in Game AI we find that Big Tech research has shaken up things quite a lot. A number of challenges were resolved in record time (Go, StarCraft, etc) and AI algorithm development is probably still increasing in speed. However, it seems that the use of AI in game-making has not changed that much, and academic research often opts for "smaller problems", slowly turning towards Human-Centered AI as possibly most important general research direction. How can we approach the next leap predicted by Alex Champandard 10 years ago of really intelligent game AI? And where would we want that? Mike presents some inconclusive thoughts and ideas on future developments. The Game AI Meetup takes place several times a year. To sign up and receive updates, please register/join here: https://www.meetup.com/game-ai-meetup-gaim-of-london/ Previous 1 Mar 2023 Next

< Back Efficient solutions for an intriguing failure of llms: Long context window does not mean LLMs can analyze long sequences flawlessly Link Author(s) Peyman Hosseini, Ignacio Castro, Iacopo Ghinassi, Matthew Purver Abstract More info TBA Link

< Back On State Representations and Behavioural Modelling Methods in Reinforcement Learning Link Author(s) H Siljebrat Abstract More info TBA Link

< Back Nuffield Research Placement IGGI was taking part for the first time in the Nuffield Research Placement scheme this year: IGGI PhD researchers were supervising keen A-level students from across the country so that the students could gain an insight into scientific research work. The supervision schemes lasted two weeks per student and took place over the summer. The students benefited from the exposure to complex problems, and their involvement in the respective project may very well give them a significant boost to their prospective university applications. One of the participating IGGI PhD Researchers, Michelangelo Conserva , reported: "It was a pleasure for me to take part in the project! During the first week I did a series of seminars to introduce the student to the relevant topics; whereas in the second week we did a project on generating synthetic faces using Generative Adversarial Networks. I personally believe that this is a great initiative that will increase diversity in the next generation of researchers and I am proud of my little contribution to it. I think that mixing online and in person meetings would be great but I understand that it was not possible this year." Another participant, Nuria Peña Pérez , provided the following feedback: "Participating in the Nuffield Research Placement scheme has been a very enriching experience. During this programme, I supervised an A-level student on a project related to my work. Before starting the project, we had several discussions about the objectives to be pursued, to which the student significantly contributed with their own ideas. The project lasted two weeks, during which the student worked hard on the development of a video game for rehabilitation, a process that allowed the student to improve their technical skills while I gained supervising experience. This programme has therefore greatly benefited both of us.I think that through the Nuffield programme students can get practical experience in research topics that might otherwise be not necessarily accessible outside of academic environments or programmes. This benefits both academia, through the incorporation of external feedback and the possibility of improving communication, and students who can develop their technical and research skills and see if they enjoy these topics before committing to specific programmes. Unfortunately, this year the program took place online, which was not a problem for the project I suggested, but I can imagine many disciplines would benefit from in-person collaborations as this would give students access to physical research labs and their equipment." IGGI is planning to take part again in next year's round which will most likely be conducted in person or in a blended format. Read here to learn more about Nuffield and the Research Placement scheme. Previous 9 Oct 2021 Next

< Back Automatic Goal Discovery in Subgoal Monte Carlo Tree Search Link Author(s) D Jeurissen, MHM Winands, CF Sironi, D Perez-Liebana Abstract More info TBA Link

< Back What makes icons appealing? The role of processing fluency in predicting icon appeal in different task contexts Link Author(s) S McDougall, I Reppa, J Kulik, A Taylor Abstract More info TBA Link

< 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?