< Back Dr Cade McCall University of York Supervisor Cade McCall is an experimental psychologist. He uses games and virtual environments to study emotion, cognition, and behaviour during threatening experiences. His work explores how threat unfolds over time as revealed by dynamics in motion tracking data, psychophysiological measures, and experience-sampling. McCall is interested in supervising projects with a psychological focus, including: ● human interactions with autonomous systems ● the use of games to manipulate emotions ● social interactions within games Research themes: Games with a purpose Player experience Game analytics Email cade.mccall@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Applied Games Game Data Player Research - Previous Next

< Back Dr Sokol Murturi Goldsmiths iGGi Alum AI for game design: learning from designers For my PhD I am investigating how AI can help developers by learning to generate content in a similar fashion to the developers themselves. I envision a framework based on reinforcement learning, where an AI can learn a design policy for some content domain (e.g., FPS maps or platformer levels) by observing human designers. The AI would learn to take particular design actions in certain kinds of content states. Recent research into reinforcement learning has shown it is a powerful framework for developing complex agent behaviours and I believe there is a lot of potential to apply this work to game design. How would a human and artificial designer interact? Assume that an AI has learned to design a specific kind of content, such as a house, by observing human designers at work. A human designer could then partially develop some new content, and ask the AI to suggest some variations on it (see figure below), with both AI and human iterating on the design in a mixed-initiative interaction. The AI could learn from feedback from both the human designer and playtesting. As human feedback may not produce enough data for effective learning, the AI could perhaps extend this with data from simulated playtests. Game design decisions are often made with an expectation of how the player will react, and I could also look at how player models could be incorporated into the AI designer. In a reinforcement learning approach, the state could represent content+player, and the AI could learn to take design actions aimed a specific types of player. Developers could use this framework to develop content targeted at an individual player's style. Moreover, if the AI has learned something about how the human designer creates content, it can then be used live during the game to modify game elements in response to player interaction. Developers could set up modular levels, giving the AI the ability to adapt certain areas with content generated specifically to match the player. Email smurt001@gold.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Enhancing Programming Learning with AI-Generated Contextual Examples in Digital Creativity Themes Game AI - Previous Next

< Back Prof. Paul Cairns University of York iGGi Chair Supervisor Paul Cairns is a professor interested in Human-Computer Interaction (HCI) generally and specifically on how games work to produce the experiences that players really value. He has looked extensively at immersion and engagement in games but is also developing new ideas on players experiences of challenge and uncertainty. He has been teaching HCI for over twenty years and is particularly interested in the rigorous application of research methods having co-edited the first book on research methods for HCI and written another about doing better statistics in HCI. He strongly believes in self-explanatory book titles. He is also Scholar-in-Residence at The AbleGamers Charity, based in the USA, through which he is working with players and game developers to inform and advance the development of accessible games. With his colleagues there, he produced the Accessible Player Experiences (APX) design patterns and card deck. He is particularly interested in supervising students with a HCI, behavioural sciences, media or computer science background on the following topics: Understanding player experiences Developing new measures of player experience whether based on self-report, physiological or other instruments Accessible player experiences Using games to understand and inform people’s experiences with other interactive systems Research themes: Accessible Games Games with a Purpose Player Experience Email paul.cairns@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Accessibility Applied Games Game Data Player Research - Previous Next

< Back Dr Mihail Morosan University of Essex iGGi Alum Computational Intelligence and Game Balance. (Industry placement at MindArk) Game design has been a staple of human ingenuity and innovation for as long as games have been around. From sports, such as football, to applying game mechanics to the real world, such as reward schemes in shops, games have impacted the world in surprising ways. This process can, and should, be aided by automated systems, as machines have proven to be capable of finding innovative ways to complement human intuition and inventiveness. When man and machine cooperate, better products are created and the world has only to benefit. My research seeks to find, test and assess methods to apply computational intelligence to human-led game balance. Early research has proven that AI can successfully aid game designers in analysing the viability of various game rules and I intend to document this and polish the techniques that will result from my work. To achieve this, I am making use of cutting edge algorithms, powerful AI techniques and novel methods. Most of the current work done involves the use of evolutionary algorithms, as well as statistical analysis and evaluation of intelligent agents in various video games. Programmer (with a focus on optimisation and quick deliverables, mostly due to competitive experience), gamer (games are fun, relaxing and a great social experience), technology consumer (comes with the programmer bit) and all around happy guy stumbling through the world. Once ended up in a management internship at a bank thinking the application was for a programming position. And another time told an interviewer that "buying and eating a burger to solve hunger" is a legitimate problem-solving skill. Somehow received an invitation to the next interview stage. Email me@morosanmihail.com Website LinkedIn Mastodon BlueSky GitHub Other Link Featured Publication(s): Automating game-design and game-agent balancing through computational intelligence Lessons from testing an evolutionary automated game balancer in industry Genetic optimisation of BCI systems for identifying games related cognitive states Online-Trained Fitness Approximators for Real-World Game Balancing Evolving a designer-balanced neural network for Ms PacMan Speeding up genetic algorithm-based game balancing using fitness predictors Automated game balancing in Ms PacMan and StarCraft using evolutionary algorithms Themes Design & Development Game AI Player Research - Previous Next

< Back Dr Daniel Hernandez University of York iGGi Alum With the games industry as his target, Daniel Hernandez’s main research objective is to design and implement algorithms that, without any prior knowledge, generate strong gameplaying agents for a wide variety of games. To tackle this “from scratch” learning, he uses, and contributes to, the fields of Multiagent Reinforcement Learning, Game Theory and Deep learning. Self-play is the main object of study in his research. Self-play is a training scheme for multiagent systems in which AIs are trained by acting on an environment against themselves or previous versions of themselves. Such training scheme bypasses obstacles faced by many other training approaches which rely on existing datasets of expert moves or human / AI agents to train against. Daniel’s hope is that further development in Self-play will allow game studios of all sizes to generate strong AI agents for their games in an affordable manner. A storyteller by nature, Daniel has a strong track record of outreach through talks and workshops both in the UK and internationally. By sharing his journey, insights and discoveries he hopes to both inspire and instruct students, researchers and developers to realise the potential that Reinforcement Learning has to improve the games industry. His passionate work on Machine learning goes beyond crafting strong gameplaying agents. He sees the potential of using AI to simplify and automate a wide range of tasks in the games industry. He has led successful projects which used machine learning aimed at automating multiagent game balancing to alleviate the burden of manual game balancing. Daniel received an MEng in Computing: Games, Vision & Interaction from Imperial College London. Wanting to combine the power of AI and the creativity of videogames, Daniel began a PhD journey to explore the misty lands of Multi Agent Reinforcement Learning (MARL). Please note: Updating of profile text in progress Email Website LinkedIn Mastodon BlueSky GitHub Other Link Featured Publication(s): A comparison of self-play algorithms under a generalized framework A generalized framework for self-play training Metagame Autobalancing for Competitive Multiplayer Games Themes Game AI Player Research - 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 Email ryan.spick@hotmail.co.uk Website LinkedIn Mastodon BlueSky GitHub Other Link 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 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 Shringi Kumari University of York iGGi Alum Shringi is a seasoned game designer with more than nine years of experience making games for companies including EA, Zynga, Bigpoint, and Wooga. She became a researcher four years ago, wondering how game designers can take inspiration from other creative fields. In her PhD, she is now studying how stage magic can be translated to games for creating believable illusions of choice and moments of surprise. She continues to consult as a game designer for companies and has started a lecturership in game design at University of East London. In the past years she has spoken about game design across the world at a number of known platforms: Indiecade Europe, Develop, Game Happens, SOMA Chicago, GDC India to count some. As a creative, she engages in working on disruptive design both in games and beyond. Her work reflects her Indian background and discusses universal issues of identity, need for diversity and the idea or illusion of home. She has recently published her debut poetry collection,“The Saree Shop” and has featured in a short story anthology with her story ”Garden of Vaginas”. Shringi is supervised by Dr Sebastian Deterding (York) and Dr Gustav Kuhn (Goldsmiths). Please note: Updating of profile text in progress Email Website LinkedIn Mastodon BlueSky GitHub Other Link Featured Publication(s): The role of uncertainty in moment-to-moment player motivation: a grounded theory Why game designers should study magic Investigating uncertainty in digital games and its impact on player immersion Studying General Agents in Video Games from the Perspective of Player Experience The Magician's Choice: Providing illusory choice and sense of agency with the Equivoque forcing technique. Design Inspiration for Motivating Uncertainty in Games using Stage Magic Principles Themes Player Research - Previous Next

< Back Dr Memo Akten Goldsmiths iGGi Alum Real-time, interactive, multi-modal media synthesis and continuous control using generative deep models for enhancing artistic expression Real-time, interactive, multi-modal media synthesis and continuous control using generative deep models for enhancing artistic expression. This research investigates how the latest developments in Deep Learning can be used to create intelligent systems that enhance artistic expression. These are systems that learn – both offline and online – and people interact with and gesturally ‘conduct’ to expressively produce and manipulate text, images and sounds. The desired relationship between human and machine is analogous to that between an Art Director and graphic designer, or film director and video editor – i.e. a visionary communicates their vision to a ‘doer’ who produces the output under the direction of the visionary, shaping the output with their own vision and skills. Crucially, the desired human-machine relationship here also draws inspirations from that between a pianist and piano, or a conductor and orchestra – i.e. again a visionary communicates their vision to a system which produces the output, but this communication is real-time, continuous and expressive; it’s an immediate response to everything that has been produced so far, creating a closed feedback loop. The key area that the research tackles is as follows: Given a large corpus (e.g. thousands or millions) of example data, we can train a generative deep model. That model will hopefully contain some kind of ‘knowledge’ about the data and its underlying structure. The questions are: i) How can we investigate what the model has learnt? ii) how can we do this interactively and in real-time, and expressively explore the knowledge that the model contains iii) how can we use this to steer the model to produce not just anything that resembles the training data, but what *we* want it to produce, *when* we want it to produce it, again in real-time and through expressive, continuous interaction and control. Memo Akten is an artist and researcher from Istanbul, Turkey. His work explores the collisions between nature, science, technology, ethics, ritual, tradition and religion. He studies and works with complex systems, behaviour, algorithms and software; and collaborates across many disciplines spanning video, sound, light, dance, software, online works, installations and performances. Akten received the Prix Ars Electronica Golden Nica in 2013 for his collaboration with Quayola, ‘Forms’. Exhibitions and performances include the Grand Palais, Paris; Victoria & Albert Museum, London; Royal Opera House, London; Garage Center for Contemporary Culture, Moscow; La Gaîté lyrique, Paris; Holon Design Museum, Israel and the EYE Film Institute, Amsterdam. Please note: Updating of profile text in progress Email memo@memo.tv Website LinkedIn Mastodon BlueSky GitHub Other Link Featured Publication(s): Top-Rated LABS Abstracts 2021 Deep visual instruments: realtime continuous, meaningful human control over deep neural networks for creative expression Deep Meditations: Controlled navigation of latent space Learning to see: you are what you see Calligraphic stylisation learning with a physiologically plausible model of movement and recurrent neural networks Mixed-initiative creative interfaces Learning to see Real-time interactive sequence generation and control with Recurrent Neural Network ensembles Collaborative creativity with Monte-Carlo Tree Search and Convolutional Neural Networks Sequence generation with a physiologically plausible model of handwriting and Recurrent Mixture Density Networks Deepdream is blowing my mind All watched over by machines of loving grace: Deepdream edition Realtime control of sequence generation with character based Long Short Term Memory Recurrent Neural Networks Themes Game AI - Previous Next

< Back Amy Smith Queen Mary University of London iGGi PG Researcher Available for post-PhD position After completing a BA in Fine Art, at Bath School of Art and Design, Amy spent some years as a tattoo artist travelling and creating artworks. An interest in learning to code then led her to complete a conversion Masters degree in Computer Science at the University of Birmingham. Keen to preserve her interests in both a creative practice as well as a new interest in generative deep learning, Amy joined the IGGI program to explore these interests further under the guidance of Dr. Mike Cook, Dr. James Walker and Prof. Simon Colton. Amy's research is currently focused on the intersection between 'imaginative play', computational creativity and generative deep learning. This project explores whether the kind of novel text, image and video media produced by generative deep learning algorithms can be used to provoke and stimulate the imaginative, ideation and visualisation capabilities of the user as they interact with this cutting edge technology. To date, her work has been published in the International Conference on Computational Social Science, AAAI, ICCC, CHI, SIGGRAPH Asia, and EvoMusArt. Amy hopes to further encourage and explore the fruits of a close collaboration between human creativity and creative AI. Email amyelizabethsmith01@gmail.com Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisors: Dr Mike Cook Prof. Simon Colton Dr James Walker Featured Publication(s): Herding CATs: Making Sense of Creative Activity Traces Tracing Creativity: A Design Space For Creative Activity Traces in HCI Interpretive Cultures: Resonance, randomness, and negotiated meaning for AI-assisted tarot divination AI Séance: Recounts from designing artificial intelligence for transcendence, interpretive lenses and chance Clip-guided gan image generation: An artistic exploration Scaling Analysis of Creative Activity Traces via Fuzzy Linkography Fuzzy Linkography: Automatic Graphical Summarization of Creative Activity Traces AI-Generated Imagery: A New Era for the 'Readymade' The @artbhot Text-To-Image Twitter Bot. Trash to Treasure: Using text-to-image models to inform the design of physical artefacts Clip-guided gan image generation: An artistic explorationClip-guided gan image generation: An artistic exploration Art and the science of generative AI Generative Search Engines: Initial Experiments Themes Creative Computing Player Research - Previous Next

< Back Dr Charline Foch University of York iGGi Alum Charline first came to the UK in 2011 to study English and Film Studies at King’s College London, before going on to a MSc in Film, Exhibition and Curation at the University of Edinburgh. By chance, accident or fate, she stumbled into the games industry, working in an independent game studio in Berlin, where she touched upon customer support, community management, content writing and QA for a new MMORPG. This experience gave her the push to start a PhD in video games. In her spare time, she is an avid film viewer, volleyball player, and amateur artist. Charline’s research focuses on how people conceptualise failure, with an emphasis on its perceived positive, desirable effects on player experience. Throughout her PhD, she has conducted research among video games players to gain a better understanding of what they perceive as the purpose and value of failure in the games they play; and conducted research among video games developers to gain a better understanding of what processes, obstacles, and ideas go into the design and implementation of failure in their games. With a focus on single-player, more narrative-driven games, she has used this research to design a cards-based design toolkit to support game designers in approaching the question of fail states and player experience in the early stages of the game development process, helping them reflect on the intersection between failure, game mechanics, storytelling, and player experience when working on their games. Aside from her PhD, Charline has also worked with the Digital Creativity Labs on the PlayOn! project, a European project gathering 9 theatres across Europe working on immersive technologies (VR, AR, apps for audience participation...) and theatre productions. During her time at PlayOn!, she has worked on the connections between the games industry and the performance arts, investigating how technology, game design principles, and theatre can work together, and what barriers practitioners face when attempting to reconcile all sides in a single production through experimentation and collaboration. Email charline.foch@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisor: Dr Ben Kirman Featured Publication(s): “The game doesn't judge you”: game designers’ perspectives on implementing failure in video games “Slow down and look”: Desirable aspects of failure in video games, from the perspective of players. Themes Design & Development Player Research - Previous Next

< Back Prof. Sebastian Deterding iGGi Responsible Innovation Lead Supervisor Sebastian Deterding is a designer-researcher working on playful, gameful, motivational, and eudaimonic design. His work asks how we might re-design the socio-technical rule systems we live in to enable a good life for all. He is founder of the Gamification Research Network, and co-editor of The Gameful World (MIT Press, 2015). An internationally recognised leader of gamification research, he is frequently invited to keynote and speak at venues like Lift, Interaction, GDC, Games Learning Society, Google, IDEO, and MIT, and his work has been covered by The Guardian, The New Scientist, the Los Angeles Times, arte, and EDGE Magazine among others. As a senior research fellow at the Digital Creativity Labs, Sebastian works on the intersection of AI, machine learning, and design for augmented creativity: how can we create systems that learn to automatically adapt and serve optimally engaging content to users, and serve optimally supportive design suggestions and tutorials to creators? He is particularly interested in supervising students with a design, HCI, or behavioural sciences background on the following topics: understanding and designing for uncertainty, curiosity, and epistemic emotions in games applied games for decarbonisation and climate adaptation design for behaviour change Self-determination theory and games Research themes: Game Design Games with a Purpose Computational Creativity Player Experience Gamification Email sebastian.deterding@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Applied Games Creative Computing Design & Development Player Research - Previous Next

< Back Filip Sroka Queen Mary University of London iGGi PG Researcher Available for placement 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. An avid LEGO collector and investor, Filip brings a unique blend of technical and creative abilities to his work. He is excited about the potential of the Metaverse and is driven by the role of technology in shaping its future. A description of Filip's research: His research explores the integration of Dynamic Difficulty Adjustment (DDA) and Procedural Content Generation (PCG) into VR rhythm games to optimise motor learning and skill acquisition. By leveraging learning theories, the project creates personalised, adaptive training environments. Beyond commercial gaming, this framework demonstrates how adaptive systems can be used to maximise engagement and training efficiency, offering high-value insights for game developers, immersive tech creators, and the digital health and fitness industry. Email f.sroka@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisors: Dr Alena Denisova Dr Laurissa Tokarchuk Dr Jeremy Gow Themes Applied Games Game AI Immersive Technology - Previous Next