Search Results

< Back Dr Abi Evans University of York Supervisor Abi Evans is a Lecturer in Interactive Media in the Department of Theatre, Film, Television, and Interactive Media at the University of York. Her research is at the intersection of Human-Computer Interaction (HCI) and Learning Sciences, exploring how technology can provide real-time adaptive scaffolding for the skills and processes associated with effective learning in a variety of settings. Abi is particularly interested in supervising students who want to create and evaluate games and immersive experiences for learning or develop approaches for measuring learning in games. Her current project focuses on developing experiences for people who are learning to code, specifically tackling barriers to learning such as imposter syndrome and misconceptions about coding concepts. Abi would also welcome students interested in games for learning in other disciplines and in informal settings as well as traditional academic disciplines. abi.evans@york.ac.uk Email Mastodon https://www.abigailevans.org/ Other links Website https://www.linkedin.com/in/abi-evans-7294379 LinkedIn Twitter Github Themes Design & Development Immersive Technology Player Research - Previous Next

< Back Dr Daniel Gomme University of Essex iGGi Alum Players have underlying expectations of the opponents they play against in strategy games: don't break the rules, provide a sense of tension, be able to communicate plans... AI doesn't always fulfil these. Dan's focus is on finding ways to better fulfil those expectations - and even to overtly change them - in order to improve player experience. With qualitative tools and in-game testing, he's found several concrete design mechanisms that can further that goal. daniel.gomme@yahoo.co.uk Email Mastodon Other links Website https://www.linkedin.com/in/daniel-gomme/ LinkedIn https://www.twitter.com/dan_gomme Twitter https://github.com/OctarineSourcerer Github Supervisor Prof. Richard Bartle Featured Publication(s): Player Expectations of Strategy Game AI Playing with Dezgo: Adapting Human-AI Interaction to the Context of Play Strategy Games: The Components of A Worthy Opponent Distributed Social Multi-Agent Negotiation Framework For Incomplete Information Games Tools To Adjust Tension And Suspense In Strategy Games: An Investigation Themes Design & Development Game AI Player Research - Previous Next

< Back Dr Raluca Gaina Queen Mary University of London iGGi Outreach Coordinator 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. r.d.gaina@qmul.ac.uk Email Mastodon https://rdgain.github.io/ Other links Website https://www.linkedin.com/in/raluca-gaina-347518114/ LinkedIn https://www.twitter.com/b_gum22 Twitter https://www.github.com/rdgain Github Featured Publication(s): PyTAG: Tabletop Games for Multi-Agent Reinforcement Learning PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games 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 Themes Game AI - Previous Next

< Back Dr Cristina Dobre Goldsmiths iGGi Alum Cristina Dobre has a background in Mathematics and Computing receiving distinction in her undergraduate degree in Computer Science. My current focus is on the nonverbal cues that influence and shape the social interaction in immersive VR environments. More broadly, I'm investigating autonomous agents (or virtual humans) in social settings in terms of non-verbal interactions with users. I'm interested in the underlying mechanics of social interaction that help developing an emphatic and engaging virtual human. At the moment, I'm working on ML models based on multimodal datasets to detect various social cues (such as gaze) or various human-defined social attitudes (such as engagement) in social interactions in VR. I'm also interested in generating more complex behaviour for virtual characters (NPCs) that will improve the user's experience with the NPCs in a social VR setting. Designing communication and other social interactions in immersive VR can be a challenging task, and aspects on this are addressed in my research. The findings from these studies can help game designers and game developers determine the appropriate non-player character's non-verbal (and verbal) behaviour in games, especially in VR games. Along with its applications in the games industry, the findings would be useful for other applications such as designing multi-modal human-machine interactions and other systems for medical purposes, for social anxiety disorders therapy, simulations, training or learning. cristina.dobre@uni-a.de Email https://hci.social/@ShesCristina Mastodon Other links Website https://linkedin.com/shesCristina LinkedIn https://www.twitter.com/shesCristina Twitter https://www.github.com/shesCristina Github Featured Publication(s): Social Interactions in Immersive Virtual Environments: People, Agents, and Avatars Rolling Horizon Co-evolution in Two-player General Video Game Playing Using machine learning to generate engaging behaviours in immersive virtual environments More than buttons on controllers: engaging social interactions in narrative VR games through social attitudes detection Nice is Different than Good: Longitudinal Communicative Effects of Realistic and Cartoon Avatars in Real Mixed Reality Work Meetings Immersive Machine Learning for Social Attitude Detection in Virtual Reality Narrative Games Direct Gaze Triggers Higher Frequency of Gaze Change: An Automatic Analysis of Dyads in Unstructured Conversation Themes Game AI Immersive Technology - Previous Next

< Back James Goodman Queen Mary University of London iGGi PG Researcher James has picked up degrees in Chemistry, History, Mathematics, Business Administration and Machine Learning. After a career in Consultancy and IT Project Management he is now finally doing the research he always wanted to. James is interested in opponent modelling, theory of mind and strategic communication in multi-player games, and how statistical forward planning can be used in modern tabletop board-games (or other turn-based environments). With a constrained budget, how much time should an agent spend thinking about it's own plan versus thinking about what other players might be doing to get in the way. How does this balance vary across different games? His secondary research interests are in using AI-playtesting as a tool for game-balancing and game-design. james.goodman@qmul.ac.uk Email Mastodon https://www.tabletopgames.ai/ Other links Website https://www.linkedin.com/in/james-goodman-b388791/ LinkedIn Twitter Github Supervisors: Dr Diego Pérez-Liébana Prof. Simon Lucas Featured Publication(s): Seeding for Success: Skill and Stochasticity in Tabletop Games From Code to Play: Benchmarking Program Search for Games Using Large Language Models Skill Depth in Tabletop Board Games Measuring Randomness in Tabletop Games A case study in AI-assisted board game design Following the leader in multiplayer tabletop games PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games MultiTree MCTS in Tabletop Games Visualizing Multiplayer Game Spaces TAG: Terraforming Mars Fingerprinting tabletop games PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games AI and Wargaming Metagame Autobalancing for Competitive Multiplayer Games Does it matter how well I know what you’re thinking? Opponent Modelling in an RTS game Weighting NTBEA for game AI optimisation Re-determinizing MCTS in Hanabi Noise reduction and targeted exploration in imitation learning for abstract meaning representation parsing UCL+ Sheffield at SemEval-2016 Task 8: Imitation learning for AMR parsing with an alpha-bound Themes Design & Development 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 Twitter 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 Janet Gibbs Goldsmiths iGGi Alum Janet is exploring how multi-modal perceptual feedback contributes to a player's sense of presence in the virtual world. Jaron Lanier described Virtual Reality (VR) as the substitution of the interface between a person and their physical environment with an interface to a simulated environment. This interface is of particular significance in understanding how presence depends on the nature, extent and veridicality of our sensorimotor interaction with the virtual environment, and how that relates to our normal engagement with the real world. In practice, only selected parts of the interface are substituted - we are never fully removed from our physical environment. Our perceptual apparatus evolved to make sense of changing sensations in multiple modalities originating naturally and coherently from the same event or percept. By contrast, in VR, individually crafted feedback using different technologies for each modality are coordinated to appear as if from a single source. VR benefits from a long history of visual and audio technologies, developed in harness for virtual experiences from cinema to computer games. Haptics is a relative newcomer that must be blended with them to create coherent multimodal perceptual experiences. Additionally, haptics is closely related to proprioception, and to the wide range of tactile senses—texture, heat, pain etc—that current VR systems do not address. Building on sensorimotor theory of perception, Janet aims to establish how our perceptual system responds to multi-modal feedback that almost, but not quite, matches what we are used to, in making sense of the simulated environment of VR. JGIBB016@gold.ac.uk Email Mastodon Other links Website LinkedIn Twitter Github Featured Publication(s): Investigating Sensorimotor Contingencies in the Enactive Interface A comparison of the effects of haptic and visual feedback on presence in virtual reality Novel Player Experience with Sensory Substitution and Augmentation Investigating sensorimotor contingencies in the enactive interface Themes - Previous Next

< Back Alex Flint University of York iGGi PG Researcher Available for placement Alex has an academic background in Psychology and Human-Computer Interaction, and their Master’s dissertation comparing measures of perceived challenge and demand in video games was published at the recent CHI 2023 conference. Alex currently works as a Research Operations Consultant for PlaytestCloud and a freelance Games User Researcher. They are also a Student Video Games Ambassador for UKIE, and regularly volunteer at conferences such as CHI Play and the GamesUR Summit. When they aren’t at their desk, you can find Alex figure skating or DJing 80’s rock. Alex’s research focuses on levelling up the narrative testing practices of indie video game developers. Narrative testing is a specialised games user research (GUR) practice that requires resources and knowledge not easily accessible to indie developers, meaning they are often disadvantaged compared to their larger AAA counterparts. Thus, Alex's work proposes the direct study of indie developers to level the playing field by democratising narrative testing best practices and empowering non-research team members to conduct GUR activities. Alex aims to achieve this goal by: Defining narrative testing best practices. Identifying key challenges indie developers face when evaluating narrative. Co-designing and evaluating narrative testing prototype(s). Assessing methods for disseminating GUR knowledge. The successful completion of this work will impact how indie studios conduct narrative testing, ultimately leading to the creation of better games. alex.flint@york.ac.uk Email Mastodon https://alexflint.tech Other links Website https://www.linkedin.com/in/alexlflint/ LinkedIn https://twitter.com/alexlflint Twitter Github Supervisor: Dr Alena Denisova Dr Jon Hook Featured Publication(s): Comparing Measures of perceived challenge and demand in video games: Exploring the conceptual dimensions of CORGIS and VGDS Faking handedness: Individual differences in ability to fake handedness, social cognitions of the handedness of others, and a forensic application using Bayes’ theorem Themes Design & Development Player Research - Previous Next

< Back Dr Fiona McNab University of York Supervisor During a postdoc at the Karolinksa Institute in Stockholm, Fiona investigated working memory and attention, providing empirical support for a role for the basal ganglia in the control of access to working memory and identification of changes in the dopamine system related to working memory training. At The Wellcome Trust Centre for Neuroimaging, UCL, with a Wellcome Trust Career Development Fellowship, she designed the working memory game in the large-scale smartphone study; “The Great Brain Experiment ”, leading to studies of different types of distraction in younger adults as well as in healthy ageing. In 2013 she moved to Birmingham University, where she conducted fMRI and behavioural studies of attention and working memory, and behavioural studies of the effects of competition on working memory. Fiona is now a lecturer in the Department of Psychology at the University of York. She is using fMRI and behavioural studies to investigate what limits working memory, how different types of distractors are successfully ignored and how working memory changes through development, with healthy aging, as well as in certain patient groups. Part of her work uses data from a new set of working memory games, which are currently available to play (York Memory Games, YORMEGA ). She is particularly interested in supervising students on the following topics: Understanding the limitations of working memory and the role of attention using games Understanding age-related changes in cognition using games, Cognitive training using games. Research themes: Game Design Games with a Purpose Player Experience Gamification Games for Cognition Research Games for Cognitive Training fiona.mcnab@york.ac.uk Email Mastodon https://www.york.ac.uk/psychology/staff/academicstaff/fm841/ Other links Website LinkedIn https://www.twitter.com/fionamcnab3 Twitter Github Themes Applied Games - Previous Next

< Back Dr Valerio Bonometti University of York iGGi Alum Game analytics and player psychology: creating reliable models of player motivation Motivation can be loosely defined as a process of the brain and the mind, capable of driving and deeply shaping human behaviour. Motivational processes are embedded in many everyday life situations, exerting their effects via a wide range of incentive mechanisms and objects. Understanding this process in a videogame context, however, requires a more holistic approach considering not just the incentive properties of the game but also the player personal characteristics. My project aims to create reliable cross-games models of player motivation taking into account the contribution of natural inter individual variability. This will be accomplished linking in-game behavioural data and psychological models via a hybrid approach, where findings from small scale experimental studies (hypothesis-driven) will guide the realization of large scale (data-driven) applications for predicting players' characteristics, future behaviour and motivation evolution. Being able to model player motivation and predict the trajectories of its evolution could possibly lead to personalized and dynamic engagement strategies able to adapt accordingly to the player characteristics and in-game behaviour. Achieving a similar goal would be of pivotal importance in industrial and gamification contexts. I obtained my bachelor degree in Psychological Science and my master degree in Clinical Psychology at Padova University (Italy). During my academic path I acquired knowledge in general psychology, cognitive psychology, psychophysiology, neuroscience and research methodology. After my master degree I spent a considerable amount of time as a research trainee, both abroad and in my country, always investigating the reward process and its effects in various contexts. During this period I worked on various projects across different fields ranging from psychophysiology, player research and game analytics. In my free time I enjoy practicing indoor climbing and travelling, I like figurative art in general and more specifically I’m a huge cinema and graphic-novel enthusiast. Supervisors: Prof. Anders Drachen, Dr Sam Devlin Please note: Updating of profile text in progress Email Mastodon Other links Website LinkedIn Twitter Github Featured Publication(s): From Theory to Behaviour: Towards a General Model of Engagement Modelling early user-game interactions for joint estimation of survival time and churn probability Predicting skill learning in a large, longitudinal MOBA dataset Mind the gap: Distributed practice enhances performance in a MOBA game Approximating the Manifold Structure of Attributed Incentive Salience from Large-scale Behavioural Data: A Representation Learning Approach Based on Artificial Neural Networks Themes Player Research - Previous Next

< Back Matt Bedder University of York iGGi Alum Abstraction-Based Monte Carlo Tree Search. (Industry placement at PROWLER.io) Monte Carlo Tree Search is a popular artificial intelligence technique amongst researchers due to the remarkable strength by which it can play many games. This technique was prominently used as the basis for AlphaGo, the AI by Google DeepMind that became the first of its kind to beat professional human players at the game Go. But despite lots of interest from academics into Monte Carlo Tree Search, the technique has seen little use in the games industry - due in part to how it is not fully understood, and due to how complex it is to implement into large games. Matthew’s research is looking into how game abstractions can be used to help implement and optimise Monte Carlo Tree Search into existing commercial video games. Semi-automated methods for domain abstraction are being investigated, with the aim of making it fast and easy for game developers to be able to implement Monte Carlo Tree Search into their products, and to exploit the wealth of academic research into this technique. Matthew is currently studying towards his PhD at the University of York, having previously graduated for the Department of Computer Science with a MEng in Computer Science with Artificial Intelligence. Before starting his PhD, Matthew spent a year at BAE Systems Advanced Technology Centre working on contracts with the European Space Agency, and has performed research into vertebrae models of Parkinson's disease with York Centre for Complex Systems Analysis. Please note: Updating of profile text in progress Email Mastodon Other links Website https://linkedin.com/pub/matthew-bedder/80/2a7/a51/ LinkedIn https://www.twitter.com/@bedder Twitter Github Featured Publication(s): Characterization and classification of adherent cells in monolayer culture using automated tracking and evolutionary algorithms Computational approaches for understanding the diagnosis and treatment of Parkinson's disease Automated motion analysis of adherent cells in monolayer culture Themes Game AI - Previous Next

< Back Ross Fifield University of York iGGi PG Researcher I am a user-centred games designer and researcher with a background in both practical and theoretical dimensions of play. I hold a BA and MA in Games Design from Falmouth University and have recently been engaged in teaching further and higher education courses in games development. My work sits at the intersection of design innovation, player psychology, and emerging technology, with a particular focus on how people find, engage with, and sustain play in social contexts. Currently undertaking a PhD as part of the iGGi programme, my research investigates the social and psychological factors that influence whether and how individuals choose to play with others. I aim to develop actionable insights that reduce barriers to engagement, support better player matchmaking, and encourage more inclusive and sustainable multiplayer experiences. I am particularly interested in live data applications and their potential to inform adaptive matchmaking systems and enhance game discoverability. My practice draws from speculative and disruptive design methodologies, with a commitment to developing future-proof solutions that benefit academic, educational, and commercial communities alike. I maintain professional interests in affective psychology, applied games, ludic lexicology, and pedagogical design. As a player, I take an agnostic approach to genre, though I have a particular affinity for First Person Shooters, MMOs, sandbox games, and live-action roleplay. I am seeking placement opportunities with studios and organisations that are open to collaboration on live, data-driven projects focused on social play, player engagement, and game discoverability. My goal is to contribute meaningfully to real-world game development while refining methodologies that support more empathetic, inclusive, and dynamic player experiences. ross.fifield@york.ac.uk Email https://bsky.app/profile/rossfifield.bsky.social Mastodon http://www.rossfifield.com Other links Website https://www.linkedin.com/in/rossfifield/ LinkedIn https://twitter.com/RossFifield Twitter Github Supervisors: Dr Joe Cutting Prof. Paul Cairns Themes Player Research - Previous Next