< Back Dr Myat Aung University of York iGGi Alum Immersion is a state in which players are engaged to a degree of total absorption that inhibits the ability to correctly report one’s surroundings or time. Present theory on immersion has developed a coherent model that provides sufficient evidence to distinguish itself from other cognitive concepts such as presence, attention, selective attention, absorption and flow. However, immersion research thus far has been hindered by difficulties with taking in-vivo measurements of cognition and physiological responses during videogame play. This presents an ideal opportunity for implementations of neuroimaging methods to carry out such real time measurements of attention, as well as other cognitive processes and their roles in videogame immersion. Using various combinations of neural and physiological methods such as skin conductance, eye tracking, electroencephalography and even functional magnetic resonance imaging, it is now possible to obtain richer data in immersion research. The goal of this project is to apply such methods in order to better define and measure videogame immersion, identify the cognitive processes and hierarchical models that are involved in immersion and ultimately contribute to the literature in videogame immersion. Though neuroimaging is limited by statistical sensitivity, challenging experimental logistics and non-ideal lab environments, they are still presently the best tools available to obtain fine-grain data of attention and the many other cognitive components of immersion. Such knowledge would contribute significantly to a better understanding of effective development of videogames, as well as educational tools. I am an MPsych Psychology graduate from the University of York, having studied Psychology, Cognitive Neuroscience & Neuroimaging for four years. My Master’s research was primarily in vision, attempting to manipulate and record parahippocampal responses to visual stimuli selected parametrically by computer algorithms. During my degree I also spent much of my time researching videogames, studying the literature on the effects of videogame play on sleep, and working with a IGGI PhD student as a lab assistant. Between my degree and my PhD, I have also been working as a data analyst at Digital Creativity Labs researching skill learning in large gaming populations from Riot Games’ League of Legends. Please note: Updating of profile text in progress Email Website LinkedIn Mastodon BlueSky GitHub Other Link Featured Publication(s): Different rules for binocular combination of luminance flicker in cortical and subcortical pathways Investigating the non-disruptive measurement of immersive player experience The trails of just cause 2: spatio-temporal player profiling in open-world games Predicting skill learning in a large, longitudinal MOBA dataset Themes Game AI - 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 Website LinkedIn Mastodon BlueSky GitHub Other Link 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

< Back Dr William Smith University of York Supervisor William Smith is a Reader in the Computer Vision and Pattern Recognition research group in the Department of Computer Science at the University of York. He is currently a Royal Academy of Engineering/The Leverhulme Trust Senior Research Fellow and an Associate Editor of the journal Pattern Recognition. His research interests span vision, graphics and ML. Specifically, physics-based and 3D computer vision, shape and appearance modelling and the application of statistics and machine learning to these areas. The application areas in which he most commonly works are face/body analysis and synthesis, surveying and mapping, object capture and inverse rendering. A wide variety of tools and areas of maths are often useful in his research such as: convex optimisation, nonlinear optimisation, manifold learning, learning/optimisation on manifolds, computational geometry and low level computer vision (e.g. features and correspondence). He leads a team of five PhD students and one postdoc and has published over 100 papers, many in the top conferences and journals in the field. He was General Chair for the ACM SIGGRAPH European Conference on Visual Media Production in 2019 and is Program Chair for the British Machine Vision Conference in 2020. Research themes: Game AI Game Design Computational Creativity Graphics and rendering Content creation Email william.smith@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Creative Computing Design & Development Game AI Player Research - Previous Next

< Back Dr Raluca Gaina Queen Mary University of London iGGi Alum + Supervisor Dr Raluca D. Gaina is currently a Lecturer in Game AI at Queen Mary University of London, where she obtained her Ph.D. in Intelligent Games and Games Intelligence in May 2021 (in the area of rolling horizon evolution in general video game playing). She completed a B.Sc. and M.Sc. in Computer Games at the University of Essex in 2015 and 2016, respectively. In 2018, she did a 3-month internship at Microsoft Research Cambridge, working on the Multi-Agent Reinforcement Learning in Malmo Competition (MARLO). She was the track organiser of the Two-Player General Video Game AI Competition (GVGAI) 2016-2019 and was the Vice-Chair for Conferences of the IEEE CIS Games Technical Committee in 2020. Her research interests include general video game playing AI, evolutionary algorithms, and tabletop games. Email r.d.gaina@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Featured Publication(s): PyTAG: Tabletop Games for Multi-Agent Reinforcement Learning The n-tuple bandit evolutionary algorithm for automatic game improvement Population seeding techniques for rolling horizon evolution in general video game playing Automatic Game Tuning for Strategic Diversity Analysis of vanilla rolling horizon evolution parameters in general video game playing General video game for 2 players: Framework and competition General Video Game Artificial Intelligence Playing with evolution Rolling horizon evolutionary algorithms for general video game playing Self-adaptive rolling horizon evolutionary algorithms for general video game playing Rolling Horizon NEAT for General Video Game Playing Frontiers of GVGAI Planning Planning in GVGAI Efficient heuristic policy optimisation for a challenging strategic card game General video game artificial intelligence Optimising level generators for general video game AI 'Did you hear that?' Learning to play video games from audio cues Project Thyia: A forever gameplayer Tackling sparse rewards in real-time games with statistical forward planning methods General video game ai: A multitrack framework for evaluating agents, games, and content generation algorithms The Multi-Agent Reinforcement Learning in Malm\" O (MARL\" O) Competition VERTIGØ: visualisation of rolling horizon evolutionary algorithms in GVGAI General win prediction from agent experience League of Legends: A Study of Early Game Impact Self-adaptive MCTS for General Video Game Playing The 2016 two-player gvgai competition Introducing real world physics and macro-actions to general video game AI Rolling horizon evolution enhancements in general video game playing Learning local forward models on unforgiving games A local approach to forward model learning: Results on the game of life game Themes Game AI - Previous Next

< Back Peter York University of York iGGi Alum PhD student working in analytics and machine learning for esports broadcast and understanding. In particular working with Weavr on various projects related to broadcast and learning tools for Dota 2. Please note: Updating of profile text in progress Email Website LinkedIn Mastodon BlueSky GitHub Other Link Featured Publication(s): Data-Driven Audience Experiences in Esports Metagaming and metagames in Esports DAX: Data-Driven Audience Experiences in Esports A generalized framework for self-play training Themes Esports Game AI - Previous Next

< Back Rob Homewood Goldsmiths iGGi Alum Personalised Aesthetics for Games The worldwide games industry is a huge market and as the spectrum of people who spend time playing games increases, there is more and more competition to create games that capture the attentions of a wide audience. Whilst games have been traditionally designed with specific cultural demographics in mind, a game that could dynamically match the cultural values of a range of demographics would maximize its potential market. Robert’s research looks at developing techniques for procedurally generating dynamic game assets that can be viewed as being relevant at a ‘per player’ level. He aims to do this by actively profiling a player’s social networks and building up a picture of the cultural references with which they identify. This knowledge could then be used to create game assets that match an aesthetic the player would likely feel comfortable with, allowing a more flexible decoupling between game mechanics and aesthetic during the design process. Designers could then focus on creating interesting game mechanics that could work in a variety of settings and the system would fill in the aesthetic detail based on the requirements of the individual player at run-time. Having studied in five countries, Robert is currently undertaking a PhD at Goldsmiths, University of London where he is part of the EPSRC funded IGGI (Intelligent Games and Games Intelligence) program. He also holds a Bachelor’s degree in Game Design and Production Management from the University of Abertay Dundee which included a year of studies at the George Mason University Computer Game Design Program. He also spent a year studying Serious Games at Masters level at the University of Skövde in Sweden (which has the longest running Serious Games program in the world). Robert has an active interest in the media arts field and has exhibited his work in three countries. Please note: Updating of profile text in progress Email Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Player Research - Previous Next

< Back Dan Cooke University of York iGGi PG Researcher Available for post-PhD position I have a keen interest in the world of finance and regulation and controls for prevention of crime in video games and wider financial marketplaces. I am interested in how criminals seek to exploit systems to transfer and disguise proceeds of crime or for the use of further crime. I have a background in accounting and finance and graduated with a MA in Applied Accounting from De Montfort University in 2019. Outside of my professional interests I play MMO's daily and am involved in the community for Old School Runescape and Runescape. My research interests include money laundering, secondary marketplaces in video games and user experience. A description of Dan’s research: The focus of my research is on detecting money laundering in video games using outlier detection methods to assess the risk of outliers relative to the data population. I aim to have identified money laundering risk and scale within the steam marketplace data i am working with and identify whether certain games or items have higher risk of being exploited for the purpose of money laundering purposes. My current research is looking at using anomaly scores (how severe of an outlier a data point is) to assess the risk of laundering within certain groups using a multi level grouping analysis. This aims to assess different risk levels per item and game and challenge the assumption that the whole marketplace behaves the same and identify risk variance between groups. Email dan.cooke@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisor(s): Dr David Zendle Featured Publication(s): Money laundering through video games, a criminals' playground Themes Esports Game Data Player Research - Previous Next

< Back Dr Carlos Gonzalez Diaz University of York iGGi Alum Carlos is finishing his PhD at the University of York. He holds an MSc in Serious Games at the University of Skövde (Sweden) and a BSc in Software Engineering (Spain). He is been closely connected with industry throughout his PhD, having worked in the last years for Microsoft Research, Sony Interactive Entertainment R&D, Musemio Ltd R&D and Goldsmiths, UoL; as well as done consulting for tech companies such as Unity Technologies. A description of Carlos' research: The purpose of my PhD research is to advance game technologies by democratising the use of ML techniques among non-experts through innovative tools and plugins for game engines. I developed ML specific visual scritping languages and used mixed-methods research approaches to understand how to better support developers in creating VR interactions and the challenges behind human-AI interaction. I had several technical jobs throughout my PhD, as my expertise is highly applicable in both industry and academia. Thanks to the broad range of expertise that I gathered through many years of industrial work and academic study, I can tackle the challenges emerging from the inter-disciplinary nature of modern work: where user psychology, immersive technology and artificial intelligence intersect. Please refer to my website for completely up-to-date information regarding publications. Feel free to reach out if you want more information or want to chat about my/your work. I am looking for positions starting on February 2023 onwards. Email carlos.gonzalezdiaz@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Supervisor(s): Prof. Sebastian Deterding Featured Publication(s): Prototyping by Moving for Virtual Reality Embodied, in-medium design of VR game motion controls using interactive supervised learning Automatic Game Tuning for Strategic Diversity Programming by Moving: Interactive Machine Learning for Embodied Interaction Design InteractML: Node Based Tool to Empower Artists and Dancers in using Interactive Machine Learning for Designing Movement Interaction Movement interaction design for immersive media using interactive machine learning Using Machine Learning to Design Movement Interaction in Virtual Reality Interactive machine learning for more expressive game interactions Making Space for Social Time: Supporting Conversational Transitions Before, During, and After Video Meetings InteractML: Making machine learning accessible for creative practitioners working with movement interaction in immersive media Interactive Machine Learning for Embodied Interaction Design: A tool and methodology Bodystorming in SocialVR to Support Collaborative Embodied Ideation Themes Creative Computing Design & Development Game AI Immersive Technology https://www.youtube.com/watch?v=taVry9IQUjE https://www.youtube.com/watch?v=kkKU3MyBspM https://www.youtube.com/watch?v=WHiPav2l5gA Previous Next

< Back Lizzie Vialls University of York iGGi Alum Discrete Models and Algorithms to create a more satisfying and strategic opponents For many 4x and Grand Strategy computer games (e.g. Civilisation, Europa Universalis), the player will be playing against one or more AI opponents. For many games, the AI is not clever enough to stand up to a player without being given the ability to "cheat" - ability to spawn in resources, see what the player is doing, etc. This creates an unsatisfactory opponent for a player, as it gives them opponents that fight through "cheating" over strategy or out-manoeuvring the player. The aim for my PhD is to look into the potential uses of SAT and similar to create a more satisfying and strategic opponent for players to play against in these styles of computer games. To this end, I’ll be identifying potential for improvement regarding my proposal, and once I’ve narrowed down the specifics - be it related to improving how SAT solvers can handle problems, or how better to encode AI into SAT - I will be working on ways to improve AI for turn based strategic games. Lizzie Vialls is a recent Computer Science graduate of University of Leicester, having graduated with a 2:1 and a prize for best third year project, which was the project that fueled her interest in SAT. When not searching for an errant semicolon in her code she can be found working with various online gaming communities, hunched over many a tabletop game, or attempting to make friends with the local feline populace. Please note: Updating of profile text in progress Email Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Game AI - Previous Next

< Back Dr Evelyn Tan University of York iGGi Alum Evelyn holds a master’s degree in Industrial-Organisational Psychology from University College London (UCL) and has previously worked in the HR Technology industry where she undertook projects on game-based assessment, virtual team coaching and virtual reality (VR) hiring and training. She has published her work on developing trust in virtual work teams at CHI PLAY 2019, a premier conference for games research. On the Emergence and Development of Team Cohesion in Newly Formed Virtual Teams Evelyn specialises in teamwork and team dynamics. She is interested in uncovering how cohesion emerges and develops, and to identify its predictors. Her goal is to understand how to build high-performing teams that make people want to stay and remain united in the pursuit of their shared objectives. Under IGGI, she studies virtual teams in competitive esport games, specifically newly formed ad hoc teams. By applying theories and principles from psychology, her work can be extended to address the challenges faced by real-world teams with similar characteristics, for example emergency response teams and short-term project teams. By studying team cohesion – its emergence and development – her work addresses the broader challenges of building high-performing teams which retain their members. Please note: Updating of profile text in progress Email Website LinkedIn Mastodon BlueSky GitHub Other Link Featured Publication(s): Communication Sequences Indicate Team Cohesion: A Mixed-Methods Study of Ad Hoc League of Legends Teams Less is More: Analysing Communication in Teams of Strangers Trusted Teammates: Commercial Digital Games Can Be Effective Trust-Building Tools Themes Esports Player Research - Previous Next

< Back Prof. Anders Drachen Supervisor Anders Drachen, PhD, (born 1976) is a Professor at the Department of Computer Science, with Digital Creativity Labs and Weavr at the University of York (UK). His work in games research is focused on user behavior, user experience and audience engagement and the application of data science, information systems modelling, business intelligence, design and Human-Computer Interaction in these domains. His research and professional work are carried out in collaboration with companies across the Creative Industries, from big publishers to indies. He is recognized as one of the most influential people in his domains of work and have authored over a hundred publications with international colleagues across industry and academia. Having lived and worked on four different continents, Anders Drachen has had the mixed pleasure of fending off three shark attacks in Africa and Australia. He is also the youngest Dane in history to publish a cooking book – dedicated to ice cream. Research themes: Data Science, Analytics, Machine Learning in Interactive Media Big Data, behavior- and social media analytics in the Creative Industries Data Mining and Business Informatics in the Creative Industries Data-Driven Storytelling and Audience Engagement Games User Research and User Experience in Games Data-Driven Design and Development Human-Computer Interaction Esports and Sports Analytics Behavioral/Market Analytics and Business Intelligence Entrepreneurship in the Creative Industries Blockchain and Cryptocurrencies Email anders.drachen@york.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Design & Development Esports Game Data Player Research - Previous Next

< Back Dr Josh Reiss Queen Mary University of London Supervisor Josh Reiss investigates transformative technologies focused around audio production and sound design. He has published more than 200 scientific papers (including over 50 in premier journals and 5 best paper awards), and co-authored two books. His research has been featured in dozens of original articles and interviews on TV, radio and in the press. He is a Fellow and former Governor of the Audio Engineering Society. He co-founded the highly successful spin-out company, LandR, and recently formed a second start-up, FXive. He maintains a popular blog, YouTube channel and twitter feed for scientific education and dissemination of research activities. Prof. Reiss has a strong interest in games research, especially procedural audio content generation. Procedural content generation supports creation of rich and varied games, maps, levels, characters and narrative elements. But sound design has not kept pace with such innovation. Often the visual aspects of every object in the scene may be procedurally rendered, yet sound designers still rely on huge libraries of pre-recorded samples. This approach is inflexible, limited and uncreative. An alternative is procedural audio, where sounds are created in real-time using software algorithms. But many procedural audio techniques are low quality, computational, or tailored only to a narrow class of sounds. Machine learning from the sample libraries, to select, optimise and improve the procedural models, could be the key to transforming the industry and creating procedural auditory worlds. He welcomes the opportunity to supervise students interested in this or related topics. Research themes: Procedural Content Generation Game Audio and Music Game AI Game Design Computational Creativity Player Experience Email joshua.reiss@qmul.ac.uk Website LinkedIn Mastodon BlueSky GitHub Other Link Themes Creative Computing Game AI Game Audio - Previous Next