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< 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 BlueSky https://github.com/maichmueller Github Supervisor(s): Prof. Simon Lucas Dr Raluca Gaina Themes Applied Games Game AI - Previous Next

< Back Emily Marriott University of Essex iGGi Alum Automated Story Generation for Games Emily is researching automated story generation for video games, focusing on the use of Planning for real-time, dynamic generation. Ideally, the stories created will reflect choices made by the player during gameplay and will update continually throughout gameplay. The aim of this research is to create a system that could be easily utilised in the development of more adaptive games. This could improve player enjoyment, increase re-playability, and allow for the inclusion or exclusion of content that may only appeal to niche audiences. Emily’s current focus is on investigating story structures and pacing to create a template for generating good stories specifically for games that are consistent, well-structured and interesting. This involves studying the pacing requirements in existing games to establish what these are and how they differ the requirements for film and TV. The system will ideally be integrated with existing game-development tools and provide an easy-to-use interface to make the creation of adaptive games easier and quicker. The eventual goal is a full story-generation system would support both the creation of quests that emerge from story requirements and a game world that fits the environment required for the story. Emily graduated from Glyndŵr University with a BSc in Computer Games Development before completing an MSc in Computer Science at Oxford Brookes University. The substance of the MSc dissertation involved generating dungeon levels and quests using grammars based on the play style the player appeared to favour. Emily enjoys playing both tabletop and computer roleplaying games, especially ones in which player actions can have a dramatic effect on the game’s progression. Please note: Updating of profile text in progress Email Mastodon Other links Website LinkedIn BlueSky Github Themes Player Research - Previous Next

< Back Prof. William Latham Goldsmiths iGGi Co-Investigator Supervisor William Latham is well known for his pioneering organic computer art created in the 80s and early 90s whilst a Research Fellow at IBM in Winchester. In 1993 he moved into Rave Music setting up a small studio in Soho, creating album covers, stage designs and videos for bands including The Shamen for three years. He then worked for ten years as Creative Director and CEO of a large computer games development company, with studios in London and Brighton, creating PC and console games published by Vivendi Universal, SONY and Warner Bros. Among the games he has produced were Evolva for Virgin Interactive, and the hit game The Thing for Vivendi Universal for Xbox, PlayStation, PC. based on the famous John Carpenter horror movie set in Antarctica. In 2007, he became a Professor in Computing at Goldsmiths, where he works on research projects with Imperial College, York University, and the Oxford Weatherall Institute. His recent "Mutator VR" Sci-Fi art experience developed at Goldsmiths for the HTC Vive has been exhibited to much acclaim in galleries and museums Shanghai, Venice, Kyoto, Dusseldorf and St. Petersburg. William was an undergraduate student at Christchurch College, Oxford University, and a postgraduate student at The Royal College of Art. His book on interactive evolutionary design, “Evolutionary Art and Computers” is cited as a leading publication in this domain. He is Director of SoftV Ltd, a company which develops Neuroscience Patient mobile Games Apps for the NHS in Unity, and is a co-founder of London Geometry Ltd. w.latham@gold.ac.uk Email Mastodon https://www.mutatorvr.co.uk Other links Website https://www.linkedin.com/in/william-latham-757326/ LinkedIn BlueSky Github Themes Creative Computing Immersive Technology - Previous Next

< Back Dr Dan Franks University of York Supervisor Dr Franks is an interdisciplinary researcher and data scientist interested in AI and machine learning. He is experienced in developing and applying evolutionary computation and machine learning methods to understanding behaviour. He is an internationally recognized leader in interdisciplinary research, has published in top journals such as Science and PNAS. Some of his papers are in the top 1% of all papers for media coverage (altmetric), and his work is regularly covered by The New Scientist, National Geographic, Wired, The BBC, The Guardian, The Times, among others. As Reader in the York Centre for Cross-disciplinary Systems Analysis, Dan works on applying AI, machine learning, and agent-based modelling, to problems in other disciplines. Particular interests involve the development of machine learning methods for creating intelligent AI and for understanding complex systems. Research themes: Game AI Game Analytics daniel.franks@york.ac.uk Email Mastodon Other links Website LinkedIn BlueSky Github Themes Game AI Game Data - Previous Next

< Back Sarah Masters University of York iGGi PG Researcher Available for post-PhD position Sarah is an artist, game developer and researcher. They have an MA in Indie Game Development from Falmouth University (Distinction), where they created the city-building card game Eudaimonia. They are an active part of the games community taking part in game jams and setting up their own commercially focused studio. Sarah's work takes a research through design approach making and exploring games as an art form for change, collaborative design, speculative futures including 'ecopunk' and how we design games to meaningfully engage and entertain. Alongside a portfolio of games, their previous work includes running a workshop on Solarpunk vs Grimdark concepts. Their work also explores sustainable design and development practices to create emotional, engaging and meaningful experiences that can be a part of a greener industry and engage in climate change conversation. sarah.masters@york.ac.uk Email https://mastodon.gamedev.place/@sarah https://sarahdotgames.itch.io/ Mastodon https://sarah.games/ Other links Website https://www.linkedin.com/in/sarah-games/ LinkedIn BlueSky https://github.com/Impalpably Github Featured Publication(s): Radical Alternate Futurescoping: Solarpunk versus Grimdark Radical Alternate Futurescoping: Solarpunk versus Grimdark Better Dead than a Damsel: Gender Representation and Player Churn Themes Applied Games Design & Development Player Research Eudaimonia: A solarpunk city-building choice and consequence game - Save the world in eight years!: Fatalis - a witchy gardening game: Previous Next

< Back Dr Adrian Bors University of York Supervisor Adrian G. Bors is an Associate Professor at the University of York and has published more than 150 papers in international journals and conferences in the areas of his research interests. He is interested in supervising projects related to the application of novel artificial intelligence methods and computer vision in Game AI. One of the areas of interest is in the modelling of game characters (intelligent agent) continuously learning from their environments, able to transfer their knowledge from one stage to the next, while accumulating the information, like human/animal beings and enabling to continuously adapt to their environments. Another topic of interest is represented by conditional image and video generation for developing game environments. The conditional video/image generation will depend on certain factors that can be pre-established or be the result of self-learning by an (intelligent agent). Most existing games relying on no movement representation lack in representing realistic and continuous movement. In this direction of research, we will aim to generated video which would be consistent with realistic movement of game characters. Specific attention will be paid to modelling the interaction of the generated movement with the environment or other actors (game characters). In another direction of research, Adrian G. Bors will supervise projects in digital watermarking of 3D graphical characters. Codes will be invisible embedded and retrieved from the 3D graphics representations. The code embedded, like the DNA in human/animals, will enable the character to act in specific ways, defining behavioural traits in similarly looking graphics characters. adrian.bors@york.ac.uk Email https://www.researchgate.net/profile/Adrian-Bors Mastodon https://www-users.cs.york.ac.uk/adrian/ Other links Website https://www.linkedin.com/in/adrian-bors-32a3668/ LinkedIn BlueSky https://github.com/AdrianBors Github Themes Game AI - Previous Next

< Back Dr Luca Rossi Queen Mary University of London Supervisor Luca Rossi is a Lecturer in Artificial Intelligence at Queen Mary University of London. His research expertise lies in the areas of structural pattern recognition, machine learning, data and network science. Within the context of IGGI, he is interested in applying graph machine learning techniques, particularly graph neural networks, to the modelling and analysis of games. He is also interested in supervising projects related to behavioural analytics and privacy issues in online gaming. luca.rossi@qmul.ac.uk Email Mastodon https://blextar.github.io/luca-rossi/ Other links Website LinkedIn BlueSky Github Themes Game AI Game Data - Previous Next

< Back Dr Ahmed M. A. Sayed Queen Mary University of London Supervisor Ahmed Sayed is a Lecturer (Assistant Professor) of Big Data and Distributed Systems at the School of EECS, QMUL and leads the Scalable Adaptive Yet Efficient Distributed (SAYED) Systems Lab. He has a PhD in Computer Science and Engineering from the Hong Kong University of Science and Technology. His research interests lie in the intersection of distributed systems, computer networks and machine learning. He is an investigator on several UK and international grants totalling nearly USD$1 million in funding. His work appears in top-tier conferences and journals including NeurIPS, AAAI, MLSys, ACM EuroSys, IEEE INFOCOM, IEEE ICDCS, and IEEE/ACM Transactions on Networking. He is interested in supervising students with a background in game AI, machine learning, distributed systems, and/or creative computing, Ahmed is interested in working with students at the intersection of artificial intelligence, machine learning, and creative computing. He aims to leverage AI/ML methods, game data and player research to design intelligent game agents by creating systems that enable game agents to learn better gaming strategies, thus enhancing the gaming experience. He is open to any research proposals in that space and currently is keen on exploring solutions that are based on leveraging the emerging distributed privacy-preserving ML ecosystems on large-scale game data. If you are interested in working with him on this, please reach out to him. ahmed.sayed@qmul.ac.uk Email Mastodon http://eecs.qmul.ac.uk/~ahmed/ Other links Website https://www.linkedin.com/in/ahmedmabdelmoniem/ LinkedIn BlueSky https://github.com/ahmedcs Github Themes Creative Computing Design & Development Game AI Game Data Player Research - Previous Next

< Back Rokas Volkovas Queen Mary University of London iGGi Alum Application of Neuroevolution to General Video Game Playing In the field of artificial intelligence, great advancements in developing AI capable of playing specific games has been made over last few decades. Over the years, the potential of General Game Playing (GGP) AI, was realized, and thus a new area of research was spawned, focusing mainly on turn-based board games. Rapidly expanding, it was just recently extended to include video games and has morphed into General Video Game Playing (GVGP). The studies in this space of AI are highly attractive due to their solution capacity of being highly transferable. As the field is relatively new, there are many different paths to explore. Some effort has already been put into incorporating the established Genetic Algorithm techniques into the area. The goal of the proposed research is to further develop models using the more complex evolutionary algorithms to find generalist solutions to the problems exposed in GVGP. More specifically, the research will aim to discover the appropriate applications and the modifications necessary of approaches such as Competitive Coevolution, circumventing its drawbacks and evolving populations capable of playing multiple games. Furthermore, in addition to other methods it will be concerned with the application of models developing generalist memory on a slower scale evolution (compared to individual in a population) with continuous state perturbations, to find closer to optimum results - adapting networks of individuals to the fitness landscape. In order to reach the goals of the research a number of experiments will be conducted, using a select few video games as a base performance measure. Training the populations evolved will involve tuning the evolutionary operators as well as altering pre-designed system be- haviours to suitably compare the viability of applied procedures. The success of bridging EA with GVPG, along with its advantages and drawbacks in the field will be readily deter- mined, comparing the solutions found to those of other existing approaches. Specifically, the similarity of the behaviour in evolvability using genetic networks searching for solutions and learning theory, via neural networks, has recently been suggested. Evolution is defined to not have any foresight, but models were built showing how it can remember previously discovered solutions, which would imply that natural selection leans towards long term evolvability. Kostas Kouvaris et. al. further establishes the underlying equivalence of the approaches, applying machine learning techniques to improve the generalisation of EA. The generalization allows combining the features from previous experience to find individuals with new feature combinations, better adapted to unseen environments. Were the exploratory learning methods developed in EA to perform no less satisfactorily in the gaming industry environment, given enough sample data from a handful of well defined behaviours, the AI units could be trained to adapt to the new levels they are placed in. In theory, this would then translate to the same amount of effort producing a larger variety of content or, alternatively, producing the same amount of content with less effort, distributing the excess to other areas of development or eliminating it to lower the total production cost. Rokas is an MEng Electronic Engineering graduate from University of Southampton. Initially, pushed away from programming in school due to being taught Pascal, he realized its power in the compulsory C course in University. Applying the knowledge to building games caused the gradual shift from electronics to software development, with the 4th year modules all having the CS tag. During the undergraduate studies Rokas held the UKESF scholarship and did 2 summer internships at Imagination Technologies. Interests in game and software development got him researching neuroevolutionary machine learning for video games. Please note: Updating of profile text in progress Email Mastodon Other links Website LinkedIn BlueSky Github Featured Publication(s): Automatic Game Tuning for Strategic Diversity Practical Game Design Tool: State Explorer Extracting learning curves from puzzle games Mek: Mechanics prototyping tool for 2d tile-based turn-based deterministic games Diversity maintenance using a population of repelling random-mutation hill climbers Themes Game AI - Previous Next

PGRs (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 PG Researchers iGGi is a community formed of PG Researchers (PGRs), Supervisors, affiliated Partners from industry and academia, and supporting Administrators. The most essential part of this community is of course the group of currently over 70 active iGGi PGRs who, through their research in games and related fields, work on creating positive impact on and through games. Their research topics spread over a wide spectrum of areas, and while you can filter subjects of interest by theme, please see the individual profiles for more detailed information. Filter by iGGi Theme Accessibility Applied Games Creative Computing Design & Development Esports Game AI Game Audio Game Data Immersive Technology Player Research Filter by Location Filter by Start Year Filter by placement/work status Doruk Balcı iGGi PG Researcher Available for placement 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 Tania Dales iGGi PG Researcher Available for placement Game AI, Design & Development, Immersive Technology, Player Research Read More Alex Flint iGGi PG Researcher Available for placement Design & Development, Player Research Read More Peyman Hosseini iGGi PG Researcher Player Research, Game AI Read More Cameron Johnston iGGi PG Researcher Available for placement Design & Development, Creative Computing Read More Gorm Lai iGGi PG Researcher Creative Computing, Game AI, Design & Development Read More George Long iGGi PG Researcher Available for placement Game AI, Design & Development, Game Data Read More Sahar Mirhadi iGGi PG Researcher Available for post-PhD position Player Research Read More Zoë O’Shea iGGi PG Researcher Design & Development, Immersive Technology, Player Research Read More Prasad Sandbhor iGGi PG Researcher Available for placement Applied Games, Design & Development Read More Philip Smith iGGi PG Researcher Available for placement Applied Games, Design & Development Read More Luiza Gossian iGGi PG Researcher Available for placement Applied Games, Design & Development Read More Connor Watts iGGi PG Researcher Game AI Read More Oliver Withington iGGi PG Researcher Available for post-PhD position Game AI, Creative Computing, Design & Development Read More Nirit Binyamini Ben Meir iGGi PG Researcher Available for placement Applied Games, Design & Development, Creative Computing Read More Karl Clarke iGGi PG Researcher Available for placement Design & Development, Immersive Technology, Player Research Read More Rory Davidson iGGi PG Researcher Available for post-PhD position Player Research, Applied Games, Design & Development Read More Francesca Foffano iGGi PG Researcher Available for post-PhD position Player Research Read More Tamsin Isaac iGGi PG Researcher Available for placement Applied Games, Design & Development, Player Research Read More Bobby Khaleque iGGi PG Researcher Available for post-PhD position Creative Computing, Game AI Read More Nicole Levermore iGGi PG Researcher Available for placement Design & Development, Immersive Technology, Accessibility, Player Research Read More Sarah Masters iGGi PG Researcher Available for post-PhD position Applied Games, Design & Development, Player Research Read More Prakriti Nayak iGGi PG Researcher Available for placement Applied Games, Accessibility, Player Research Read More Younès Rabii iGGi PG Researcher Available for post-PhD position Game AI, Creative Computing, Design & Development Read More Remo Sasso iGGi PG Researcher Game AI Read More Florence Smith Nicholls iGGi PG Researcher Game AI, Creative Computing, Design & Development, Game Data Read More Sunny Thaicharoen iGGi PG Researcher Available for post-PhD position Player Research, Game Data, Esports Read More Tom Wells iGGi PG Researcher Available for placement Read More Ruizhe "Jay" Yu Xia iGGi PG Researcher Available for placement Game AI, Game Data Read More Toby Best iGGi PG Researcher Available for placement Game AI, Design & Development, Player Research Read More Dan Cooke iGGi PG Researcher Available for placement Esports, Game Data, Player Research Read More Ross Fifield iGGi PG Researcher Available for placement Player Research Read More James Gardner iGGi PG Researcher Game AI Read More Dominik Jeurissen iGGi PG Researcher Game AI, Design & Development, Game Data Read More Joshua Kritz iGGi PG Researcher Available for placement Game AI, Applied Games, Design & Development Read More Océane Lissillour iGGi PG Researcher Available for placement Design & Development, Player Research Read More Dimitris Menexopoulos iGGi PG Researcher Available for post-PhD position Game Audio, Creative Computing Read More Dien Nguyen iGGi PG Researcher Available for placement Game AI, Applied Games, Design & Development, Creative Computing Read More Erin Robinson iGGi PG Researcher Design & Development, Immersive Technology Read More Amy Smith iGGi PG Researcher Available for post-PhD position Creative Computing, Player Research Read More Filip Sroka iGGi PG Researcher Game AI, Applied Games, Immersive Technology Read More Marko Tot iGGi PG Researcher Game AI Read More Lauren Winter iGGi PG Researcher Design & Development, Player Research Read More

< Back Dr Diego Pérez-Liébana Queen Mary University of London iGGi Industry Liaison Supervisor Born in Madrid (Spain) and living in London (United Kingdom), I am a Senior Lecturer in Computer Games and Artificial Intelligence at Queen Mary University of London. I hold a PhD in Computer Science from the University of Essex (2015) and a Master degree in Computer Science from University Carlos III (Madrid, Spain; 2007). My research is centered in the application of Artificial Intelligence to games, Tree Search and Evolutionary Computation. At the moment, I am especially interested on General Video Game Playing and Strategy games, which involves the creation of content and agents that play any real-time game that is given to it, and research in Abstract Forward Models. I have recently been awarded with an EPSRC grant on Abstract Forward Models for Modern Games. I am author of more than 100 papers in the field of Game AI, published in the main conferences of the field of Computational Intelligence in Games and Evolutionary Computation. I have publications in highly respected journals such as IEEE TOG and TEVC. I have also organised international competitions for the Game AI research community, such as the Physical Travelling Salesman Competition, and the General Video Game AI Competition, held in IEEE (WCCI, CIG) and ACM (GECCO) International Conferences. I also experience in the videogames industry as a game programmer (Revistronic; Madrid, Spain), with titles published for both PC and consoles. I worked as a software engineer (Game Brains; Dublin, Ireland), where I oversaw the development of AI tools that can be applied to the latest industry videogames. I am particularly interested in supervising students with background on applications of Tree Search or Evolutionary Algorithms for strategy games. Research Themes: Game AI Rolling Horizon Evolutionary Algorithms. Monte Carlo Tree Search Statistical Forward Planning methods. Strategy Games. diego.perez@qmul.ac.uk Email Mastodon https://diego-perez.net Other links Website https://www.linkedin.com/in/diegoperezliebana/ LinkedIn BlueSky https://github.com/diegopliebana Github Themes Game AI Game Data - Previous Next

< Back Dominik Jeurissen Queen Mary University of London iGGi PG Researcher Hey, I'm Dominik Jeurissen, and I'm passionate about both software engineering and machine learning, with a particular interest in fully autonomous agents that do not rely on absurd amounts of data. My focus areas include reinforcement learning, unsupervised learning, and the emerging capabilities of large language models. I earned my MSc in Artificial Intelligence from Maastricht University and my BSc in Computer Science with a focus on Applied Mathematics from RWTH Aachen. During my undergraduate studies, I worked as a software engineer at INFORM GmbH, contributing to their supply management software, add*ONE. A description of Dominik's research: My PhD is a collaboration with Creative Assembly , focusing on researching AI for complex strategy games, such as Total War. With the recent emergence of Large Language Models (LLMs), I’m exploring their potential to enhance game-playing agents. LLMs can instantly recall knowledge on almost any topic (though not without occasional errors), perform basic reasoning, and are easily configured for a wide range of text-based tasks. These abilities make them especially promising for game development, where machine learning agents often struggle due to constantly changing game environments. d.jeurissen@qmul.ac.uk Email https://commandercero.github.io/ Mastodon Other links Website https://www.linkedin.com/in/dominik-jeurissen/ LinkedIn https://bsky.app/profile/dominikjeurissen.bsky.social BlueSky https://github.com/CommanderCero Github Supervisors: Dr Diego Pérez-Liébana Dr Jeremy Gow Featured Publication(s): Playing NetHack with LLMs: Potential & Limitations as Zero-Shot Agents PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games Generating Diverse and Competitive Play-Styles for Strategy Games PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games Automatic Goal Discovery in Subgoal Monte Carlo Tree Search Game state and action abstracting monte carlo tree search for general strategy game-playing Portfolio search and optimization for general strategy game-playing The Design Of" Stratega": A General Strategy Games Framework Themes Design & Development Game AI Game Data - Previous Next