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< Back Dr Christian Guckelsberger Queen Mary University of London iGGi Alum + Supervisor Intrinsic Motivation in Computational Creativity with Applications to Games. (Industry placement at Splash Damage and Microsoft Research) This research investigates how we can engineer artificial systems that are creative in their own right. Christian addresses this challenge with computational models of intrinsic motivation (IM). Intrinsically motivated agents perform an activity for its inherent satisfaction rather than for some instrumental outcome. A classic example is to act in order to satisfy one’s curiosity. In both theoretical and applied studies, he demonstrates that models of IM can give rise to general, robust and adaptive creative systems. Christian has shown how models of IM can be used to create highly general non-player characters. Such characters can potentially be used in a wide range of games without previous knowledge of the game mechanics, reducing costs and effort in game development while increasing robustness and behavioural variety Christian’s ongoing research stretches beyond video games, investigating the role of computational models of IM for intentional agency, open-ended development and creativity in minimal lifeforms and artificial systems. Christian studied Computer Science, History of Art and Business in Germany and the UK and is now based in London, working towards a PhD in Artificial Intelligence. His work challenges the question how computers could ever become genuinely creative with a highly interdisciplinary approach based on Computing, Cognitive Science and Philosophy. Over the last few years, he published papers on a wide range of topics, held a tutorial on intrinsic motivation in video games, organised workshops on computational serendipity and spent three months at NYU’s Game Innovation Lab for a research collaboration. Christian has substantial industry experience, looking back at three years in the R&D department of SAP SE and a recent internship at Microsoft Research Cambridge. He enjoys working in an international environment with open-minded, passionate people. Please note: Updating of profile text in progress Email Mastodon Other links Website https://linkedin.com/in/christianguckelsberger LinkedIn BlueSky Github Featured Publication(s): Not All the Same: Understanding and Informing Similarity Estimation in Tile-Based Video Games Predicting game difficulty and engagement using AI players Embodiment and computational creativity Intrinsic Motivation in Computational Creativity Applied to Videogames. PhD Thesis. 306 pages. The Relationship of Future State Maximization and von Foerster's Ethical Imperative Through the Lens of Empowerment On the Machine Condition and its Creative Expression. Understanding and Strengthening the Computational Creativity Community: A Report From The Computational Creativity Task Force. Action Selection in the Creative Systems Framework Measuring perceived challenge in digital games: Development & validation of the challenge originating from recent gameplay interaction scale (CORGIS) Generative design in Minecraft: Chronicle challenge Towards Mode Balancing of Generative Models via Diversity Weights Automating Generative Deep Learning for Artistic Purposes: Challenges and Opportunities Themes Game AI - Previous Next

< Back Lauren Winter University of York iGGi PG Researcher Lauren was introduced to gaming from an early age when they received a PlayStation One as a gift. From there, video games became a huge part of their life, exploring new worlds through the eyes of a vast array of characters. Following their undergraduate degree in Psychology with Sociology, they completed their MSc in Psychology Research Methods at the University of Nottingham. A fascination with looking for trends in data and creating complex spreadsheets in Excel led them to a job analysing student information in a school, where they also ran four Esports teams competing across three games. Their research interests primarily focus on player research in team-based PVP games and looking at players’ awareness of each other in these environments. A description of Lauren's research: Lauren’s research investigates the differences in human-human and human-AI interaction in team-based digital games. Simultaneous combinations of competitive and cooperative play are found in many high grossing games, such as Call of Duty and League of Legends. These games provide environments for players to play with strangers, friends, and AI, and elicit social presence, a term used to describe the awareness of others in a digital environment. Lauren’s research will focus on two types of social presence: cooperative presence and competitive presence. Despite the popularity of these games, little is known about the juncture between the two and the effects they have on player experience. Due to the increasing inclusion of AI in daily life, including the gaming space, investigating these effects will have implications for future research in team-based digital games, as well as in the creation of AI that works with and against users. Through the development of a bespoke game, created in Unity, Lauren will investigate how people work together and against other players and AI, identifying aspects of the AI that can be manipulated into better player experiences and more enjoyable games. lauren.winter@york.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/lauren-winter-/ LinkedIn BlueSky Github Supervisor: Prof. Paul Cairns Featured Publication(s): "Leave our kids alone!": Exploring Concerns Reported by Parents in 1-star Reviews Better Dead than a Damsel: Gender Representation and Player Churn Themes Design & Development Player Research - Previous Next

< Back Dr Thryn Henderson University of York iGGi Alum Thryn’s phd explored the practices of personal vignette games, with a particular interest in the vignette game’s approaches to digital persona, their roots in approachable DIY culture, and their importance to marginalised creators. Publications from their work can be found in the Digra 2020 archive and Persona Studies Volume 6, Issue 2 . Thryn’s interest in gaming grows from a delight in telling stories. They endeavour to find the spaces where play incorporates and encourages collaborative narrative, poetry, theatre, activism, subversion, surprise and expression. Most of Thryn’s work in playful media can be found in zines, cardboard installations, paper games, hidden screens, or roaming through the woods around the UK. They are a co-founder of the playful design co-operative Furtive Shambles, currently producing experimental live and tabletop game experiences. thrynhenderson@gmail.com Email Mastodon https://furtiveshambles.com Other links Website LinkedIn BlueSky Github Themes Design & Development - Previous Next

< Back Erin Robinson University of York iGGi PG Researcher Erin Robinson is a multimedia artist, experimental musician and PhD Researcher from London. Her work primarily involves the design of interactive installations, where she takes a participatory approach to evolving visual-scapes, but also takes form in fixed media, sound art, free improvisation, live visuals and immersive experiences. Her work critically engages with the concepts of posthumanism and postmodernism, exploring notions of authenticity and existence in the digital anthropocene by blurring lines between organic and non-organic entities, reality and virtuality, self and otherness. She is a founding member of SubPhonics, an experimental music and sound art collective based in London. Recent works include ‘Flora_Synthetica’, shown at Peckham Digital 2024, and ‘Pluriversal Perspectives: Moss’, shown at the South London Botanical Institute and Conference for Designing Interactive Systems (Copenhagen) 2024. A description of Erin's research: "My research adopts a practice-based approach to exploring participant-contributed materials, a technique positioned at the intersection of participatory and new media arts. This interactive technique enables participants to contribute aesthetic and semiotic materials to new media artworks through open forms of interaction, including but not limited to, text input, drawing, and video feed. Although both participatory and new media artistic practices involve audience engagement, traditional interactive media often impose restrictive computational frameworks. In contrast, participatory practices, typically conducted in person, allow participants greater freedom, resulting in deeper engagement and more diverse, unexpected outcomes that reflect the audience's perspectives and behaviours. This research underscores the potential of digital artworks to provide more expansive and identity-reflecting experiences by incorporating participant-contributed materials. By using strengths of participatory practices, digital artworks can achieve a richer and more personalised form of interaction, and meaningful engagement with audiences." erin.robinson@york.ac.uk Email Mastodon Other links Website LinkedIn BlueSky https://github.com/erinrrobinson Github Supervisor(s): Prof. Sebastian Deterding Themes Design & Development Immersive Technology - Previous Next

< Back Dr Nuria Peña Pérez Queen Mary University of London iGGi Alum Nuria got her bachelor’s in biomedical engineering in Spain before moving to London. After studying an MSc in Neurotechnology and working in robotic neurorehabilitation at Imperial College London, she discovered the enormous potential of serious games in the field of human-robot interaction. She joined IGGI in 2018. Her PhD research involves studying human motor control and learning during bimanual tasks to investigate how the dynamics of the interaction can serve to develop better training systems. This is done through the development of interactive gaming environments that are compatible with rehabilitation robotic devices. The modelling of the recorded human neuromuscular data allows to explore how to better help patients to restore their motor function. Her work is a collaboration between the Advanced Robotics group at Queen Mary University of London and the Human Robotics group at Imperial College London. As part of her PhD she has worked for the company GripAble, developing games for the assessment and training of hand function (February 2020-August-2020). n.penaperez@qmul.ac.uk Email Mastodon Other links Website LinkedIn BlueSky Github Supervisor(s): Dr Ildar Farkhatdinov Featured Publication(s): Redundancy Resolution in Trimanual vs. Bimanual Tracking Tasks Dissociating haptic feedback from physical assistance does not improve motor performance Bimanual interaction in virtually and mechanically coupled tasks The impact of stiffness in bimanual versus dyadic interactions requiring force exchange How virtual and mechanical coupling impact bimanual tracking Lateralization of impedance control in dynamic versus static bimanual tasks Is a robot needed to modify human effort in bimanual tracking? Exploring user motor behaviour in bimanual interactive video games Quartz Crystal Resonator for Real-Time Characterization of Nanoscale Phenomena Relevant for Biomedical Applications Illuminating Game Space Using MAP-Elites for Assisting Video Game Design Themes Applied Games - Previous Next

< Back Andrew Martin Queen Mary University of London iGGi Alum Applications in game development for programming language theory and AI Modern game development is highly iterative. Iteration is usually discussed in terms of a team completing design iterations, but can also be considered at the level of an individual developer attempting to complete a task, or experimenting with some ideas. At this level, the feedback loop provided by the tool becomes critical. Programming environments in particular often have a very poor feedback loop. Programming feedback can be thought of in terms of how quickly and seamlessly the user is able to observe the results of their work. This process is usually plagued with manual tasks and long pauses. It is common that a user will need to recompile, relaunch their program, and then manually recreate whatever state is required to observe the behaviour that they are working on. Frameworks like Elm, React and Vuejs are establishing a new norm of automatic hot-reloading with state preservation. These systems represent a branch of programming language research that is strongly focused on developer experience. In order to improve upon this work for game development, we must overcome the unique challenges that game development entails. Although the systems mentioned are all quite recent, there is a rich vein of research to draw on, which can be traced through dataflow programming, Smalltalk, Erlang, functional-reactive programming, Lisp and more. Predictive completions are considered by many to be a natural next-step in the evolution of live programming environments. An AI programming assistant would propose program fragments as completions or alternatives. The agent may seek to anticipate the user’s intent, or to provide creative suggestions. There is much relevant research in the fields of program synthesis, inductive logic programming, machine learning and genetic programming. One significant problem is how to smoothly and safely integrate a system like this into the user’s workflow. Many of the properties useful for safely enabling live programming features, such as isolation of side-effects, will also permit an AI agent to safely generate and execute code. Andy graduated from Imperial College London with an MEng in Computing in 2011. Following this he worked on game engine tools and technology at a startup called Fen Research, and then as a senior developer at a software consulting firm called LShift. In 2016 he spent six months working as a Research Associate in the Computational Creativity group at Goldsmiths, before starting his PhD. Please note: Updating of profile text in progress Email Mastodon Other links Website LinkedIn BlueSky Github Themes Game AI - Previous Next

< Back Dr Michelangelo Conserva Queen Mary University of London iGGi Alum Michelangelo Conserva is a second year PhD researcher studying principled exploration strategies in reinforcement learning. He is particularly interested in randomized exploration and, more generally, Bayesian methods for reinforcement learning. He holds a BSc in Statistics, Economics and Finance from Sapienza, University of Rome and an MSc in Computational Statistics and Machine learning from University College of London. A description of Michelangelo's research: As a PhD student at Queen Mary University of London, Michelangelo aims to leverage Bayesian models to develop principled algorithms for reinforcement learning in the context of function approximations. The main challenge lies in finding a balance between computational costs and optimality. Evaluating such balance requires careful evaluation, which is currently lacking in reinforcement learning. m.conserva@qmul.ac.uk Email Mastodon https://michelangeloconserva.github.io/ Other links Website https://www.linkedin.com/in/michelangeloconserva/ LinkedIn BlueSky https://github.com/MichelangeloConserva Github Supervisors: Prof. Simon Lucas Dr Paulo Rauber Featured Publication(s): What are you looking at? Team fight prediction through player camera Posterior Sampling for Deep Reinforcement Learning Hardness in Markov Decision Processes: Theory and Practice Recurrent Neural-Linear Posterior Sampling for Nonstationary Contextual Bandits The Graph Cut Kernel for Ranked Data Themes Game AI - Previous Next

< Back Prof. David Adger Queen Mary University of London Supervisor Inventing new languages for in-game communications; studying their effects on game play and character development. d.j.adger@qmul.ac.uk Email Mastodon Other links Website LinkedIn BlueSky Github Themes Creative Computing - Previous Next

< Back Filip Sroka Queen Mary University of London iGGi PG Researcher Filip is a Computer Science researcher specialising in Game AI. He acquired an Integrated Masters in Computer Science from Queen Mary University of London and is pursuing a PhD in Game AI with iGGi. With a passion for algorithms and problem-solving, he constantly seeks new challenges to enhance his skills. As an avid LEGO collector and investor, he brings a unique blend of technical and creative abilities. He is excited about the potential of the Metaverse and is driven by the role of technology in shaping its future. His research explores the integration of Dynamic Difficulty Adjustment (DDA) into VR rhythm games such as Beat Saber, with the goal of enhancing player skill development and motivation through the application of learning theories. By addressing difficulty spikes, the project creates personalised learning experiences using human-made maps designed to accelerate the learning process. Key components include player evaluation, map segmentation, and procedural generation. The broader aim is to extend these findings to other rhythm games, offering benefits to players, game developers, and the health and fitness industry. f.sroka@qmul.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/filip-sroka-134954197/ LinkedIn BlueSky https://github.com/FilipSroka Github Supervisor: Dr Laurissa Tokarchuk Themes Applied Games Game AI Immersive Technology - Previous Next

< Back Dr Ryan Spick University of York iGGi Alum Deep Learning for Procedural Content Generation in Virtual Environments Ryan Spick is a PhD student with a computer science background, working on methods to improve how content (models, terrain, assets etc.) is created with an autonomous focus, with the main focus on generative deep learning to augment real-world data through a series of neural network layers to learn unlying properties of these data. Ryan has published a variety of papers around his main topic of generating content, such as terrain generation using generative adversarial networks and 3D voxel coloured model generation, to collaborations on other topics using deep learning, such as death prediction in a multiplayer online game and applying a recent map-elites algorithm. He has also worked with several leading industry researchers/games companies to further develop his research skill.If you have any ideas or collaboration opportunities please get in contact through any of the mediums below. Please note: Updating of profile text in progress ryan.spick@hotmail.co.uk Email Mastodon https://www.rjspick.com/ Other links Website https://www.linkedin.com/in/ryan-spick-505b63131/ LinkedIn BlueSky Github Featured Publication(s): System and Method for Point Cloud Generation System and method for training a machine learning model Robust Imitation Learning for Automated Game Testing Behavioural Cloning in VizDoom Illuminating Game Space Using MAP-Elites for Assisting Video Game Design Utilising VIPER for Parameter Space Exploration in Agent Based Wealth Distribution Models Human Point Cloud Generation using Deep Learning Naive mesh-to-mesh coloured model generation using 3D GANs Realistic and textured terrain generation using GANs Procedural Generation using Spatial GANs for Region-Specific Learning of Elevation Data Deep Learning for Wave Height Classification in Satellite Images for Offshore Wind Access Illuminating Game Space Using MAP-Elites for Assisting Video Game Design Time to die: Death prediction in dota 2 using deep learning Themes Game AI - Previous Next

< Back Dr Chris Madge Queen Mary University of London iGGi Alum Turning Difficult Scientific Problems into Easy Games: Crowdsourcing Solutions via Gamification The aim of the research is to exploit, on a large scale, the idea introducing game elements in a non-game context (gamification) and make use of a large population of non-expert users to solve scientific problems (crowdsourcing). The proposed research follows the increasingly popular concept of splitting a large, complex task into small easily digestible tasks that lend themselves to division, distribution and game representation. This research will begin by taking advantage of the University of Essex’s expertise in the field of Natural Language Engineering. Multiple games will be created to attempt to encourage people to participate in training natural language models. This will be achieved by splitting these tasks into smaller problems that can be represented as games, and easily solved by players that could not easily be solved computationally. Alongside this, the success of different gamification methods and game design choices will be evaluated to determine their effect on the information gathered and the accuracy achieved. This evaluation will be used to guide the development of future games in the research with a view to producing better quality models for solving natural language problems, and improving gamification. Prior to starting my PhD with IGGI I completed a BSc in Computer Science and MSc in Advanced Computer Science. During both of those I took multiple computer game and AI courses in addition to text analytics and natural language engineering courses. During my BSc I was fortunate to work at Signal Media as an intern on text analytics related problems. Before starting my BSc I worked as a software developer for 5 years, primarily in web application development. I’ve had a passion for games from a very young age and continue to play on PC, mobile and consoles today. Please note: Updating of profile text in progress Email Mastodon Other links Website LinkedIn BlueSky Github Featured Publication(s): Gamifying language resource acquisition Progression in a language annotation game with a purpose Incremental game mechanics applied to text annotation Making text annotation fun with a clicker game The design of a clicker game for text labelling Crowdsourcing and aggregating nested markable annotations Testing TileAttack with Three Key Audiences Experiment-driven development of a gwap for marking segments in text Metrics of games-with-a-purpose for NLP applications Testing game mechanics in games with a purpose for NLP applications TileAttack Novel Incentives for Phrase Detectives Themes Player Research - Previous Next

< Back Oliver Withington Queen Mary University of London iGGi PG Researcher Available for post-PhD position Oliver Withington is a AI and games researcher working on novel methods for evaluating content generation systems for games. Following a successful career in the healthcare technology industry he decided to combine his life long love of games and interest in AI research into a PhD with the iGGi CDT in 2020. He lives in London with his wife and two young daughters, and when he is not writing about, thinking about, or talking about games you can probably find him in either his local bouldering gym, or in the park either pursuing or being pursued by two small children. A description of Oliver's research: Oliver's primary motivation is to make the evaluation of novel content generators more standardised, robust and straightforward for both researchers and game designers. Currently his focus is on techniques for producing informative visualisations of the output spaces of content generators. His work has been published at many of the leading conferences in his field, and he has also taken his work and ideas to the game industry, most recently in the form of a talk at GDC 2025's AI Summit. owithington@hotmail.co.uk Email Mastodon http://owithington.co.uk Other links Website https://www.linkedin.com/in/oliver-withington-909052bb/ LinkedIn BlueSky https://github.com/KrellFace Github Supervisors: Dr Jeremy Gow Dr Laurissa Tokarchuk Featured Publication(s): Exploring Minecraft Settlement Generators with Generative Shift Analysis HarmonyMapper: Generating Emotionally Divers Chord Progressions for Games. The Right Variety: Improving Expressive Range Analysis with Metric Selection Methods Visualising Generative Spaces Using Convolutional Neural Network Embeddings Compressing and Comparing the Generative Spaces of Procedural Content Generators Illuminating Super Mario Bros: quality-diversity within platformer level generation Themes Creative Computing Design & Development Game AI Previous Next