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< Back A Manifesto for More Productive Psychological Games Research Link ​ Author(s) N Ballou Abstract ​ More info TBA ​ Link

< Back Understanding whether lockdowns lead to increases in the heaviness of gaming using massive-scale data telemetry: An analysis of 251 billion hours of playtime Link ​ Author(s) D Zendle, C Flick, D Hargarth, N Ballou, J Cutting, A Drachen Abstract ​ More info TBA ​ Link

< Back If everything is a loot box, nothing is: Response to Xiao et al. Link ​ Author(s) D Zendle, P Cairns, R Meyer, S Waters, N Ballou Abstract ​ More info TBA ​ Link

< Back Eudaimonia in Digital Games Link ​ Author(s) T Cole, A Denisova, J Iacovides Abstract ​ More info TBA ​ Link

< Back Examining the effects of video game difficulty adaptation on performance and player experience Link ​ Author(s) M Frister, P Cairns, F McNab Abstract ​ More info TBA ​ Link

< Back Calligraphic stylisation learning with a physiologically plausible model of movement and recurrent neural networks Link ​ Author(s) D Berio, M Akten, FF Leymarie, M Grierson, R Plamondon Abstract ​ More info TBA ​ Link

< Back ​ Shopna Begum Queen Mary University of London iGGi Administrator iGGi Admin ​ ​ iGGi Administrator at QMUL Shopna is part of the iGGi Admin Team which is responsible for the smooth running of iGGi. In her role as iGGi QMUL Administator she provides administrative services and pastoral care to PhD students and assists the iGGi QMUL Manager in key aspects of the Centre's management. ​ shopna.begum@qmul.ac.uk Email Mastodon Other links Website LinkedIn Twitter Github ​ ​ Themes - Previous Next

< Back ​ Marko Tot Queen Mary University of London ​ iGGi PG Researcher ​ Available for post-PhD position Hello! I'm Marko, and welcome to my page! As a part of the IGGI programme and Game AI research group, I'm working on adapting Statistical Forward Planning methods for complex environments. Statistical Forward Planning methods have proven to be effective in some simpler domains and, without requiring any prior learning, they provide a good out of the box AI algorithm. However, while these algorithms shine in certain games, they struggle to perform well in cases where the reward received from the game is sparse. In games where it takes a series of optimal actions to reach the goal, without any significant feedback from the environment in between, their performance drops significantly. My research is centered on solving this problem through automatic sub-goal generation and utilisation of local learned forward models. Creation of the sub-goals could be used to simulate the feedback from the environment and give regular rewards to the agent even in sparse and complex environments. I started my journey in video games when I got my first PC at the age of six, and at that point it was decided that I'm going to make a career out of it. So here I am, ~20 years later, a PhD. student at Queen Mary University of London, trying to make AI agents that can play games, and regularly spending too much time playing games under the excuse that it's all for 'research purpose'. ​ m.tot@qmul.ac.uk Email Mastodon https://markotot.github.io/ Other links Website https://www.linkedin.com/in/markotot/ LinkedIn https://twitter.com/tot_marko Twitter https://github.com/markotot Github Supervisor(s): Dr Diego Pérez-Liébana Featured Publication(s): Turning Zeroes into Non-Zeroes: Sample Efficient Exploration with Monte Carlo Graph Search Making Something Out of Nothing: Monte Carlo Graph Search in Sparse Reward Environments What are you looking at? Team fight prediction through player camera Themes Game AI - Previous Next

< Back More than a bit of coding:(un-) Grounded (non-) Theory in HCI Link ​ Author(s) T Cole, M Gillies Abstract ​ More info TBA ​ Link

< Back Children and Young People's Involvement in Designing Applied Games: Scoping Review Link ​ Author(s) MJ Saiger, S Deterding, L Gega Abstract ​ More info TBA ​ Link

< Back ​ Kevin Denamganaï University of York ​ iGGi PG Researcher ​ Available for post-PhD position After graduating as an Engineer from the Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA), France, with two double-degree diplomas, a MEng in Electrical Engineering and Information Science from the Osaka Prefecture University (OPU), Japan, and a MRes in Artificial Intelligence and Robotics from the Université de Cergy-Pontoise (UCP), France, Kevin Denamganaï spent a year accumulating experience as a Robotics & Machine Learning freelancer. He is now putting those skills at use in the IGGI PhD program, that, among other things, gives him the opportunity to reunite with video games. Indeed, it was thanks to a keen interest towards video game creation that he started learning programming around 12. His research interests are about everything psychology, neuroscience, AI, (deep) reinforcement/imitation learning, robotics, and natural/artificial language emergence and understanding as well as human-computer interfaces, challenging the question what are the necessary components of artificial agents to be able to converse with human-beings in an engaging manner and to be able to cooperate with them towards a pre-defined goal, e.g. clearing a level in a given video game. ​ kevin.denamganai@york.ac.uk Email Mastodon https://kevindenamganai.netlify.app/ Other links Website LinkedIn https://www.twitter.com/@KeviDenam Twitter https://github.com/Near32/ Github Supervisor(s): Dr James Walker Featured Publication(s): ETHER: Aligning Emergent Communication for Hindsight Experience Replay Visual Referential Games Further the Emergence of Disentangled Representations Meta-Referential Games to Learn Compositional Learning Behaviours A comparison of self-play algorithms under a generalized framework On (Emergent) Systematic Generalisation and Compositionality in Visual Referential Games with Straight-Through Gumbel-Softmax Estimator ReferentialGym: A Nomenclature and Framework for Language Emergence & Grounding in (Visual) Referential Games A generalized framework for self-play training Coupled Kuramoto oscillator-based control laws for both formation and obstacle avoidance control of two-wheeled mobile robots Obstacle avoidance control law for two-wheeled mobile robots controlled by oscillators Themes Game AI - Previous Next

< Back TileAttack Link ​ Author(s) C Madge Abstract ​ More info TBA ​ Link