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< Back Artificial intelligence across europe: A study on awareness, attitude and trust Link ​ Author(s) Teresa Scantamburlo, Atia Cortés, Francesca Foffano, Cristian Barrué, Veronica Distefano, Long Pham, Alessandro Fabris Abstract ​ More info TBA ​ Link

< Back ​ Prof. Greg Slabaugh Queen Mary University of London ​ Supervisor ​ ​ Gregory G. Slabaugh is Professor of Computer Vision and AI and Director of the Digital Environment Research Institute (DERI) at Queen Mary University of London. He is also a Turing Fellow at the Alan Turing Institute. His research work spans computer vision and computer graphics including geometric modelling and image/video-based understanding. He is interested in deep learning approaches including generative techniques like normalizing flow an generative adversarial networks. He previously worked in the games industry as a 3D graphics programmer and his PhD thesis focussed on how to model 3D objects from a collection of images. He is interested in how to create engaging content and interaction from images as well as procedural methods to reduce the effort of 3D modelling. ​ g.slabaugh@qmul.ac.uk Email Mastodon https://www.eecs.qmul.ac.uk/~gslabaugh Other links Website https://www.linkedin.com/in/greg-slabaugh-a5b03a1/ LinkedIn Twitter Github ​ ​ Themes Applied Games Creative Computing Immersive Technology - Previous Next

< Back Sequence generation with a physiologically plausible model of handwriting and Recurrent Mixture Density Networks Link ​ Author(s) D Berio, M Akten, FF Leymarie, M Grierson, R Plamondon 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. Make Real VR

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. Monash University

< Back ​ Michael Aichmüller Queen Mary University of London ​ iGGi Alum ​ ​ My background lies in physics and statistical mathematics with a later specialization in optimization in the fields of Reinforcement Learning (RL) and Causal Inference. My first encounters with RL occurred during my Masters when studying how to create strong policies in perfect information games using algorithms, such as MinMax, MCTS, DQN, and later AlphaZero variants. My favorite game application remains the board game ‘Stratego’. In the meantime I investigated the estimation of causal parents influencing a target variable from interventional datasets for my Master’s thesis. Specifically, how well Deep Learning estimations could replace exponentially scaling graph search methods with approximations requiring only polynomial runtime. A description of Michael's research: My research focuses on the state-of-the-art in game-playing solutions for imperfect information games (think games like Poker, Stratego, Liar’s Dice etc.). I am particularly interested in the application of No-Regret (and related) methods which seek to learn those actions that provided the most benefit (or least regret) compared to the benefit all possible actions provided on average. These methods learn such via iterative play to find a Nash-Equilibrium (NE), a game-theoretic concept comparable to an optimal policy known from Single-Agent RL, but for all partaking players at once. Particularly, variants of Counterfactual Regret Minimization (CFR) remain the state-of-the-art algorithms for computing NEs in 2-player zero-sum games due to their success in tabular form so far. Yet, prohibitive complexity and memory scaling bars them from large-scale applications. Hence, research of recent years seeks to couple CFR (and other No-Regret methods) with function approximation, such as Deep Learning, to scale up the size of applicable games with already notable successes (Deepstack, Libratus, Pluribus, DeepNash). My research seeks to contribute to this endeavour by first analyzing the specifics of established methods and finding ways to introduce Hierarchical RL concepts to No-Regret learning. Please note: Updating of profile text in progress m.f.aichmueller@qmul.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/michael-aichmueller/ LinkedIn Twitter https://github.com/maichmueller Github Supervisor(s): Prof. Simon Lucas Dr Raluca Gaina ​ Themes Applied Games Game AI - Previous Next

< Back Crowdsourcing and aggregating nested markable annotations Link ​ Author(s) C Madge, J Yu, J Chamberlain, U Kruschwitz, S Paun, M Poesio Abstract ​ More info TBA ​ Link

< Back Win or learn fast proximal policy optimisation Link ​ Author(s) DS Ratcliffe, K Hofmann, S Devlin Abstract ​ More info TBA ​ Link

< Back Extracting learning curves from puzzle games Link ​ Author(s) R Volkovas, M Fairbank, JR Woodward, S Lucas Abstract ​ More info TBA ​ Link

< Back ​ Dr Shanxin Yuan Queen Mary University of London ​ Supervisor ​ ​ Dr Shanxin Yuan is a Lecturer in Digital Environment at Queen Mary University of London. He has rich expertise in deep learning, low level computer vision, and 3D digital modelling of humans from photographs. His PhD thesis focused on 3D hand pose estimation, his work is well recognized in the academia and is also deployed into commercially launched mass market mobile phones. His current research on digital humans focuses reconstructing, modelling, and rendering digital twins. He is interested in super-realistic immersive gaming, body/hand pose and facial expression retargeting, and behaviour analysis with avatars. For the new project in 2023, we are interested in working on human facial expression estimation, high-res realistic face reconstruction and rendering, face re-enactment, and face augmentation. The aim of the project is to build an editable super-realistic 3D human face model that can express novel expressions, views, shapes, and appearance, from multiple sources of input, such as images, sounds, and key points. The related techniques include deep learning, computer vision, natural language processing, and neural rendering. ​ shanxin.yuan@qmul.ac.uk Email Mastodon https://shanxinyuan.github.io/ Other links Website https://www.linkedin.com/in/shanxin-yuan-4859b656/ LinkedIn Twitter Github ​ ​ Themes Applied Games Creative Computing Game AI Immersive Technology Player Research - Previous Next

< Back Testing game mechanics in games with a purpose for NLP applications Link ​ Author(s) C Madge, J Chamberlain, R Bartle, U Kruschwitz, M Poesio Abstract ​ More info TBA ​ Link

< Back Design & Development How might we advance game-making with new design and development insights, methods, tools, and techniques? Project areas include: Integrating the physical and the digital in games Co-designing games with players who lack technical expertise Designing and understanding the impact of persuasive games << Previous Theme page Next Theme page >> iGGi >>> People <<< relevant to this Theme: Doruk Balcı iGGi PG Researcher ​ Design & Development, Player Research Read More Toby Best iGGi PG Researcher Available for placement Game AI, Design & Development, Player Research Read More Steph Carter iGGi PG Researcher Available for placement Applied Games, Design & Development, Player Research, Accessibility, Game Data Read More Dr Tom Cole iGGi Alum + Supervisor ​ Game AI, Design & Development Read More Prof. Richard Bartle Supervisor ​ Design & Development, Game AI, Player Research Read More Dr Anna Bramwell-Dicks Supervisor ​ Game Audio, Player Research, Design & Development, Applied Games, Accessibility Read More Dr Guifen Chen Supervisor ​ Creative Computing, Design & Development, Immersive Technology, Player Research Read More Dr Mike Cook Supervisor ​ Game AI, Design & Development, Creative Computing Read More Nirit Binyamini Ben Meir iGGi PG Researcher Available for placement Applied Games, Design & Development, Creative Computing Read More Terence Broad iGGi PG Researcher Available for post-PhD position Creative Computing, Design & Development Read More Karl Clarke iGGi PG Researcher Available for placement Design & Development, Immersive Technology, Player Research Read More Prof. Peter Cowling Supervisor ​ Game AI, Applied Games, Design & Development Read More Load More iGGi People working in this Theme iGGi >>> Publications <<< relevant to this Theme: Playing NetHack with LLMs: Potential & Limitations as Zero-Shot Agents D Jeurissen, D Perez-Liebana, J Gow, D Cakmak, J Kwan arXiv preprint arXiv:2403.00690, 2024 Dominik Jeurissen View Details Climate Club: A Group-based Game to Support Sensemaking of Climate Actions P Sandbhor, J Hook FDG '24: Proceedings of the 19th International Conference on the Foundations of Digital Games, Article No.: 32, Pages 1 - 12, 2024 Prasad Sandbhor Prasad Sandbhor View Details An appraisal-based chain-of-emotion architecture for affective language model game agents M Croissant, M Frister, G Schofield, C McCall Plos one 19 (5), e0301033, 2024 Maximilian Croissant Madeleine Frister View Details Use of Technology in Brief Interventions L Gega, MJ Saiger Brief CBT and Science-Based Tailoring for Children, Adolescents, and Young Adolescents, and Young Adults, Springer, pp 293-309, 2024 Michael John Saiger View Details Applying and Visualising Complex Models in Esport Broadcast Coverage A Pedrassoli Chitayat, F Block, JA Walker, A Drachen Proceedings of the 2024 ACM International Conference on Interactive Media Experiences, 2024 Alan Pedrassoli Chitayat View Details How Could They Win? An Exploration of Win Condition for Esports Narratives AP Chitayat, FO Block, JA Walker, A Drachen Annual Symposium on Computer-Human Interaction in Play (CHI PLAY 2024), 2024 Alan Pedrassoli Chitayat View Details Load More iGGi Publications for this Theme Previous Next