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< Back Dr Daniel Berio Goldsmiths iGGi Alum AutoGraff: A Procedural Model of Graffiti Form. (Industry placement at Media Molecule) The purpose of this study is to investigate techniques for the procedural and interactive generation of synthetic instances of graffiti art. Considering graffiti as a special case of the calligraphic tradition, I propose a "movement centric" alternative to traditional curve generation techniques, in which a curve is defined through a physiologically plausible simulation of a (human) movement underlying its production rather than by an explicit definition of its geometry. In my thesis, I consider both single traces left by a brush (in a series of strokes) and the extension to 2D shapes (representing deformed letters in a large variety of artistic styles). I demonstrate how this approach is useful in a number of settings including computer aided design (CAD), procedural content generation for virtual environments in games and movies, computer animation as well as for the smooth control of robotic drawing devices. Daniel Berio is a researcher and artist from Florence, Italy. Since a young age Daniel was actively involved in the international graffiti art scene. In parallel he developed a professional career initially as a graphic designer and later as a graphics programmer in video games, multimedia and audio-visual software. In 2013 he obtained a Master degree from the Royal Academy of Art in The Hague (Netherlands), where he developed drawing machines and installations materializing graffiti-inspired procedural forms. Today Daniel is continuing his research in the procedural generation of graffiti within the IGGI (Intelligent Games and Game Intelligence) PhD program at Goldsmiths, University of London. Please note: Updating of profile text in progress Email Mastodon Other links Website LinkedIn BlueSky Github Featured Publication(s): Optimality Principles in the Procedural Generation of Graffiti Style SURFACE: Xbox Controlled Hot-wire Foam Cutter The role of image characteristics and embodiment in the evaluation of graffiti Emergence in the Expressive Machine The CyberAnthill: A Computational Sculpture Sketch-Based Modeling of Parametric Shapes Artistic Sketching for Expressive Coding Calligraphic stylisation learning with a physiologically plausible model of movement and recurrent neural networks Sequence generation with a physiologically plausible model of handwriting and Recurrent Mixture Density Networks AutoGraff: Towards a computational understanding of graffiti writing and related art forms Kinematics reconstruction of static calligraphic traces from curvilinear shape features Interactive generation of calligraphic trajectories from Gaussian mixtures Sketching and Layering Graffiti Primitives. Kinematic Reconstruction of Calligraphic Traces from Shape Features Expressive curve editing with the sigma lognormal model Dynamic graffiti stylisation with stochastic optimal control Computer aided design of handwriting trajectories with the kinematic theory of rapid human movements Generating calligraphic trajectories with model predictive control Learning dynamic graffiti strokes with a compliant robot Computational models for the analysis and synthesis of graffiti tag strokes Towards human-robot gesture recognition using point-based medialness Transhuman Expression Human-Machine Interaction as a Neutral Base for a New Artistic and Creative Practice Themes Game AI - Previous Next

< Back Dr Paulo Rauber Queen Mary University of London Supervisor I am a lecturer in Artificial Intelligence at Queen Mary University of London. Before becoming a lecturer, I was a postdoctoral researcher in the Swiss AI lab working on reinforcement learning under the supervision of Jürgen Schmidhuber. I believe that intelligence should be defined as a measure of the ability of an agent to achieve goals in a wide range of environments, which makes reinforcement learning an excellent framework to study many challenges that intelligent agents are bound to face. p.rauber@qmul.ac.uk Email Mastodon https://paulorauber.com/ Other links Website LinkedIn BlueSky https://github.com/paulorauber Github Themes Game AI - Previous Next

< Back Alex Fletcher Queen Mary University of London iGGi Alum Alex Fletcher is a freelance audio engineer and junior game developer working on understanding the perceived flow and player experiences in mobile rhythm games and how a dynamic difficulty adjustment system would improve these experiences. The function of EEG and other biosensors as an additional measurement of player experience is of particular interest as further research in its use as an adaptive system. Other areas of research interest include game-based learning and games with a purpose. Please note: Updating of profile text in progress Email Mastodon Other links Website https://www.linkedin.com/in/alex-fletcher-64ab72176 LinkedIn BlueSky Github Themes Applied Games Game Audio Player Research - Previous Next

< Back Dr Joe Cutting University of York iGGi Alum + Supervisor Dr Joe Cutting is a Lecturer in Human-Computer Interaction in the Department of Computer Science at the University of York, UK. He has a BSc in Computer Science and an MSc in Cognitive Science from the University of Birmingham and completed an IGGI PhD at the University of York in 2019. Much of his research is in the area of the effects of playing video games on outcomes such as learning, cognitive abilities, wellbeing and behaviour change. This includes new psychological theories of how learning happens in video games and how game play can affect mental health, as well as studies on how game play can prevent cognitive decline in older people. He is also creating applied games to address current issues in education such as student wellbeing and teacher recruitment. Before becoming an academic, Joe enjoyed a varied career which included working as an interactive producer for the London Science Museum and founding his own digital startup company in the area of applied games. joe.cutting@york.ac.uk Email Mastodon https://www.cs.york.ac.uk/people/jcutting Other links Website LinkedIn BlueSky Github Supervisor(s): Prof. Paul Cairns Featured Publication(s): The Relationship Between Lockdowns and Video Game Playtime: Multilevel Time-Series Analysis Using Massive-Scale Data Telemetry Four grand challenges for video game effects scholars: How digital trace data can improve the way we study games Measuring the experience of playing self-paced games Measuring game experience using visual distractors Four dilemmas for video game effects scholars: How digital trace data can improve the way we study games The many faces of monetisation: Understanding the diversity and extremity of player spending in mobile games via massive-scale transactional analysis Busy doing nothing? What do players do in idle games? Understanding whether lockdowns lead to increases in the heaviness of gaming using massive-scale data telemetry: An analysis of 251 billion hours of playtime Themes Applied Games Design & Development Player Research - Previous Next

< Back Susanne Binder Queen Mary University of London iGGi Manager iGGi Admin iGGi Manager @ QMUL ; alongside David Hull (iGGi Manager @ UoY) , and supported by Shopna Begum , Helen Tilbrook and Oliver Roughton, she's mostly in charge of making things run at iGGi with particular focus on iGGi-QMUL-specific admin iGGi-QMUL-specific student concerns PR, website and social media industry liaison s.binder@qmul.ac.uk Email https://dizl.de/@sus4nn3b1nd3r/ Mastodon Other links Website https://www.linkedin.com/in/susanne-binder-b1184647/ LinkedIn https://bsky.app/profile/susannebinder.bsky.social BlueSky Github Themes - Previous Next

< 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