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< Back Dr Andrew James Wood University of York Supervisor I am an interdisciplinary researcher at the University of York. My background is in Mathematical Physics but my interests are now in applying computational and mathematical techniques to interesting problems, mostly in Biology. This includes such topics as collective motion (particularly in interaction networks and the role of noise) and microbiology (particularly in metabolism, industrial biotechnology, spatial structure and plasmid dynamics) as well as modelling naval conflicts and glycosylation. I have a natural interest in games and am interested in the interface between games and science, be that in using games to do, or disseminate, science or in utilising mechanisms and insights from research to inspire games. Research themes: Game Analytics Game Design Games with a Purpose Gamification jamie.wood@york.ac.uk Email Mastodon https://ajamiewood.weebly.com/ Other links Website https://www.linkedin.com/in/jamie-wood-82460055/ LinkedIn BlueSky Github Themes Applied Games Design & Development Game Data - Previous Next

< Back Dr Yizhao Jin Queen Mary University of London iGGi Alum Currently a student at Queen Mary University of London (QMUL), I have delved deep into the realms of artificial intelligence and game design. With a passion for understanding the complexities behind real-time strategy (RTS) games and their dynamic, unpredictable nature, I have committed myself to contribute novel insights to this domain. Research: My primary research area is Hierarchical Reinforcement Learning (HRL) for Real-Time Strategy (RTS) games. RTS games, known for their intricate mechanics and vast decision spaces, present a formidable challenge for traditional AI approaches. By employing HRL, I aim to develop agents that can not only understand the multi-layered tactics and strategies of these games but also learn to adapt to ever-changing game scenarios efficiently. The main objectives of my research are: Better Generalization: To create agents that can seamlessly transition between different RTS games or various maps within the same game without extensive retraining. This involves understanding common strategic threads across multiple game domains. Efficient Training: RTS games are inherently time-consuming due to their vast decision spaces and prolonged gameplay. My research seeks ways to optimize the training process, ensuring that AI agents can learn faster and with fewer computational resources. acw596@qmul.ac.uk Email Mastodon Other links Website LinkedIn BlueSky https://github.com/decatt Github Supervisors: Prof. Greg Slabaugh Prof. Simon Lucas Themes Game AI Previous Next

< Back Dr Daniel Gomme University of Essex iGGi Alum Players have underlying expectations of the opponents they play against in strategy games: don't break the rules, provide a sense of tension, be able to communicate plans... AI doesn't always fulfil these. Dan's focus is on finding ways to better fulfil those expectations - and even to overtly change them - in order to improve player experience. With qualitative tools and in-game testing, he's found several concrete design mechanisms that can further that goal. daniel.gomme@yahoo.co.uk Email Mastodon Other links Website https://www.linkedin.com/in/daniel-gomme/ LinkedIn BlueSky https://github.com/OctarineSourcerer Github Supervisor Prof. Richard Bartle Featured Publication(s): Player Expectations of Strategy Game AI Playing with Dezgo: Adapting Human-AI Interaction to the Context of Play Strategy Games: The Components of A Worthy Opponent Distributed Social Multi-Agent Negotiation Framework For Incomplete Information Games Tools To Adjust Tension And Suspense In Strategy Games: An Investigation Themes Design & Development Game AI Player Research - Previous Next

< Back Matt Bedder University of York iGGi Alum Abstraction-Based Monte Carlo Tree Search. (Industry placement at PROWLER.io) Monte Carlo Tree Search is a popular artificial intelligence technique amongst researchers due to the remarkable strength by which it can play many games. This technique was prominently used as the basis for AlphaGo, the AI by Google DeepMind that became the first of its kind to beat professional human players at the game Go. But despite lots of interest from academics into Monte Carlo Tree Search, the technique has seen little use in the games industry - due in part to how it is not fully understood, and due to how complex it is to implement into large games. Matthew’s research is looking into how game abstractions can be used to help implement and optimise Monte Carlo Tree Search into existing commercial video games. Semi-automated methods for domain abstraction are being investigated, with the aim of making it fast and easy for game developers to be able to implement Monte Carlo Tree Search into their products, and to exploit the wealth of academic research into this technique. Matthew is currently studying towards his PhD at the University of York, having previously graduated for the Department of Computer Science with a MEng in Computer Science with Artificial Intelligence. Before starting his PhD, Matthew spent a year at BAE Systems Advanced Technology Centre working on contracts with the European Space Agency, and has performed research into vertebrae models of Parkinson's disease with York Centre for Complex Systems Analysis. Please note: Updating of profile text in progress Email Mastodon Other links Website https://linkedin.com/pub/matthew-bedder/80/2a7/a51/ LinkedIn BlueSky Github Featured Publication(s): Characterization and classification of adherent cells in monolayer culture using automated tracking and evolutionary algorithms Computational approaches for understanding the diagnosis and treatment of Parkinson's disease Automated motion analysis of adherent cells in monolayer culture Themes Game AI - Previous Next

< Back Francesca Foffano University of York iGGi PG Researcher Available for post-PhD position Francesca's work represents her fascination with how players elaborate and understand complex situations in video games. She likes to use mixed methods (both qualitative and quantitative) to understand high-level player perception in video games using her competencies in HCI (MSc at the University of Trento) and Psychology (BSc at the University of Padua). Prior to joining the PhD, she developed international experience in industry and research. She worked as Research Fellow on AI and ethics for the European project AI4EU at ECLT (Ca' Foscari University of Venice) and on players' perception of adaptive videogames at Reykjavik University. She also was involved as UX Strategist in creative content for MediaMonks headquarter (Amsterdam). A description of Francesca's research: Players will tell you exactly when they got stuck playing a game, but how we define stuck in the first place is still open to discussion. This PhD research aims to identify how and when this happens to help in predicting when players need support. The goal is to smoothen the player experience by reducing the need for external support (such as online guides, walkthroughs, and online forums) that might affect player immersion. The current stage of the research uses in-depth interviews to understand what players have in common, no matter what task they are doing or game they are playing. So why rely on user tests that consider singular test cases instead of understanding where they originate? ff716@york.ac.uk Email Mastodon https://ffoffano.wordpress.com/about/ Other links Website https://www.linkedin.com/in/foffanofrancesca/ LinkedIn https://bsky.app/profile/francescafoffano.bsky.social BlueSky Github Supervisor: Prof. Paul Cairns Featured Publication(s): A Survey on AI and Ethics: Key factors in building AI trust and awareness across European citizens. When Games Become Inaccessible: A Constructive Grounded Theory on Stuckness in Videogames Artificial intelligence across europe: A study on awareness, attitude and trust When Games Become Inaccessible: A Constructive Grounded Theory on Stuckness in Videogames Investing in AI for social good: an analysis of European national strategies European Strategy on AI: Are we truly fostering social good? Changes of user experience in an adaptive game: a study of an AI manager Themes Player Research - Previous Next

< Back Dien Nguyen Queen Mary University of London iGGi PG Researcher Available for placement I graduated from the University of California, Irvine with a BSc in Computer Game Science and a Minor in Statistics. My undergraduate thesis focused on augmenting Monte Carlo tree search with a value network trained through a self-play framework similar to AlphaZero. During my undergraduate degree, I became interested in the intersection of games and artificial intelligence—applying methods of reinforcement learning, graphical models, and knowledge representation to game playing and game design. My long-term goal is to work on the problem of formalizing game elements, representing game systems in a way that allows for automatic reasoning and inference. I also enjoy playing games where I can customize and theorycraft my playstyle to satisfy certain gameplay fantasies while beating the game. My current research is within the field of Automated Game Design Learning, an emerging field in AI research with the purpose of learning game design models through playing. The current strategy is to play out the full game in thousands of iterations, which can be impractical for complex games with large state space and computationally expensive forward models. My research will focus on applying Go-Explore—a recent exploration paradigm that outperforms many state-of-the-arts—to improve the efficiency of automated playtesting of tabletop games by using an archive of interesting game states to reduce the time needed for self-play. The research will be primarily conducted within the TAG framework and aim to be game-agnostic. On successful completion, this research will improve game development cycles, resulting in higher-quality games, and potentially give unique insights into the game design process. d.l.nguyen@qmul.ac.uk Email Mastodon Other links Website LinkedIn BlueSky Github Supervisor: Dr Diego Pérez-Liébana Featured Publication(s): Unveiling modern board games: an ML-based approach to BoardGameGeek data analysis Themes Applied Games Creative Computing Design & Development Game AI - Previous Next

< Back Dr Pengcheng Liu Queen Mary University of London Supervisor Dr Pengcheng Liu is a Lecturer (Assistant Professor) at the Department of Computer Science, University of York, UK. He is an internationally leading expert in robotics, Artificial Intelligence and human-machine interaction. He has been leading and involving in several research projects, including EPSRC, Innovate UK, Horizon 2020, Erasmus Mundus, FP7-PEOPLE, HEIF, NHS I4I, NSFC, etc. Several of his research works were published on top-tier journals and leading conferences in the fields of robotics and AI. Before joining York, he has held several academic positions including a Senior Lecturer at Cardiff School of Technologies, Cardiff Metropolitan University, UK, a joint Research Fellowship at Lincoln Centre for Autonomous Systems (LCAS) and Lincoln Institute of Agri-Food Technology (LIAT), University of Lincoln, UK, a Research Assistant and a Teaching Assistant at Bournemouth University, UK. I also held academic positions as a Visiting Fellow at Institute of Automation, Chinese Academy of Sciences, China and Shanghai Jiao Tong University, China. Dr Liu is a Member of IEEE, IEEE Robotics and Automation Society (RAS), IEEE Systems, Man and Cybernetics Society (SMC), IEEE Control Systems Society (CSS) and IFAC. He is member of IEEE Technical Committees (TC) on Bio Robotics, Soft Robotics, Robot Learning, and Safety, Security and Rescue Robotics. He has published over 60 journal and conference papers. Dr Liu serves as an Associate Editor for IEEE Access and PeerJ Computer Science. He received the Global Peer Review Awards from Web of Science in 2019, and the Outstanding Contribution Awards from Elsevier in 2017. He was selected as regular Fundings/Grants reviewer for EPSRC, NIHR and NSFC. Dr Liu’s research interest relevant to CDT IGGI include applied games for healthcare and rehabilitation applications, as well as using mixed reality and machine learning for human-machine interactions. He is particularly interested in supervising students with a design, HCI, computer science or behavioural sciences background on the following topics: applied games for healthcare and rehabilitation design for adaptive mixed reality system for physical therapy and neurological rehabilitation design for physical and cognitive behaviour change learning for human intention prediction analysis of mixed reality rehabilitation system with biological signals (EEG, sEMG) pengcheng.liu@york.ac.uk Email Mastodon https://sites.google.com/view/pliu Other links Website https://www.linkedin.com/in/pengcheng-liu-12703288/ LinkedIn BlueSky Github Themes Applied Games Game AI Immersive Technology - Previous Next

< Back Prof. Richard Bartle University of Essex iGGi Co-Investigator Supervisor Richard Bartle is a renowned pioneer in game design and research. He co-wrote the first virtual world, MUD ("Multi-User Dungeon") in 1978, and has thus been at the forefront of the online games industry from its very inception. He is an influential writer on all aspects of virtual world design, development, and management. As an independent consultant, he has worked with many of the major online game companies in the U.K. and the U.S. over the past 30 years. His 2003 book, Designing Virtual Worlds , has established itself as a foundation text for researchers and developers of virtual worlds alike. His Player Type theory is taught in game design programmes worldwide (he appears in examination questions!). His interests are directed mainly virtual worlds, particularly Massively Multiplayer Online Role-Playing Games (MMORPGs, or MMOs), but cover all aspects of game design. He is keen to see AI used for non-player characters in MMOs (his PhD is in AI), and his current work considers the long-term moral and ethical implications of this. They’re maybe not what you might think they were at first glance… rabartle@essex.ac.uk Email Mastodon https://mud.co.uk/richard/ Other links Website https://www.linkedin.com/in/richardbartle/ LinkedIn BlueSky Github Themes Design & Development Game AI Player Research - Previous Next

< Back James Gardner University of York iGGi PG Researcher I am a third-year PhD student at The University of York, specialising in computer vision and machine learning for 3D scene understanding. Supervised by Dr William Smith, my research focuses on neural-based vision and language priors in inverse rendering and scene representation learning. I'm particularly interested in neural fields, generative models, 3D computer vision, differentiable rendering, geometric deep learning, multi-modal models, and 3D scene understanding in general. My research has been recognised with publications at prestigious conferences including NeurIPS and ECCV. Currently, I am working as a research fellow on the ALL.VP project, funded by BridgeAI and Dock10, developing relightable green screen performance capture using deep learning and inverse rendering techniques. This work aims to provide greater creative control to film and TV productions without requiring expensive LED volumes or post-production. I hold an MEng in Electronic Engineering from The University of York, for which I was awarded the IET Prize for outstanding performance and the Malden Owen Award for the best-graduating student on an MEng programme. A description of James' research: My research lies at the intersection of computer vision, machine learning, and 3D scene understanding, with a particular focus on neural-based approaches and the integration of vision and language priors. My work spans a range of topics including neural fields, generative models, differentiable rendering, and geometric deep learning. A key theme in my research is the use of 3D inductive biases for inverse rendering, addressing challenges such as illumination estimation, albedo/geometry disentanglement, and shadow handling in complex outdoor scenes. I've made contributions in creating a rotation-equivariant neural illumination model and spherical neural models for sky visibility estimation in outdoor inverse rendering. Additionally, my work extends to learning rotation-equivariant latent representations of the world from 360-degree videos, aimed at advancing the field of 3D scene understanding and developing models with an understanding of core physical principles such as object permanence. Through my research, I aim to build computer systems capable of deeply comprehending the 3D world, utilising self-supervised, generative, and non-generative approaches to push the boundaries of what's possible in computer vision and scene representation learning. james.gardner@york.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/jadgardner/ LinkedIn BlueSky https://jadgardner.github.io/ Github Featured Publication(s): The Sky's the Limit: Relightable Outdoor Scenes via a Sky-Pixel Constrained Illumination Prior and Outside-In Visibility Themes Game AI - Previous Next

< Back Dr Piers Williams University of Essex iGGi Alum Partial Observability as a game mechanic There is a wide variety of different types of games, each providing its own unique challenge to artificial intelligence. Not all games provide full access to the environment, creating interest and difficulty by hiding particular pieces of information from the player. Other types of game expect teamwork from the players rather than being solely adversarial. Some games use both restrictions, and it is this type of game that this thesis concentrates on. Piers graduated from the University of Essex with an MSc in Computer Science. His interests lie in the field of Artificial Intelligence and in particular Multi-Agent Systems. Please note: Updating of profile text in progress Email Mastodon Other links Website LinkedIn BlueSky Github Featured Publication(s): Hexboard: A generic game framework for turn-based strategy games Evaluating and Modelling Hanabi-Playing Agents Monte carlo tree search applied to co-operative problems The 2018 hanabi competition Artificial intelligence in co-operative games with partial observability Ms. Pac-Man Versus Ghost Team CIG 2016 Competition Cooperative games with partial observability Themes Game AI - Previous Next

< Back Dr Claudio Guarnera University of York Supervisor You can get more out of your site elements by making them dynamic. To connect this element to content from your collection, select the element and click Connect to Data. Once connected, you can update it anytime without affecting your design or updating elements by hand. Add any type of content to your collection, such as rich text, images, videos and more, or upload it via CSV file. You can also collect and store information from your site visitors using input elements like custom forms and fields. Be sure to click Sync after making changes in a collection, so visitors can see your newest content on your live site. claudio.guarnera@york.ac.uk Email Mastodon https://www.cs.york.ac.uk/cvpr/member/claudio/ Other links Website https://www.linkedin.com/in/giuseppe-claudio-guarnera LinkedIn BlueSky Github Themes Applied Games Creative Computing - Previous Next

< Back Dr Mona Jaber Supervisor Mona Jaber is a lecturer in Internet of Things (IoT) who’s research is centred at the intersection of IoT and machine learning for sustainable development goals. In particular, she is interested in harnessing IoT data to model mobility trends in a digital twin platform that allows users to test future measures in a verisimilar virtual environment. Her research is grounded in privacy-preserving measures for capturing and analysing IoT data. She is the winner of a new investigator award research grant (DASMATE £500K) in which she examines distributed acoustic sensors systems and a privacy-preserving alternative data source to model active travel. She is interested in supervising students on the topic of serious game building that engages the public in shaping their neighbourhood through interventions in the virtual environment towards sustainable 15 minutes city goals. m.jaber@qmul.ac.uk Email Mastodon http://eecs.qmul.ac.uk/profiles/jabermona.html Other links Website https://www.linkedin.com/in/mona-jaber/ LinkedIn BlueSky Github Themes Accessibility Applied Games Game AI - Previous Next