Search Results

< Back Dr Yul HR Kang Queen Mary University of London Supervisor Yul Kang, MD, PhD is a computational cognitive neuroscientist studying how natural & artificial neural networks handle unavoidable uncertainty in sequential decision-making, such as wayfinding during navigation. He uses Bayesian approaches and probabilistic neural representation models, with applications to games, fundamental science, and healthcare. He received his MD in Seoul National University (South Korea), PhD in Columbia University (USA), and did postdoctoral research at the University of Cambridge (UK), where he was elected and served as a Junior Research Fellow. His work was published in top-tier journals such as Current Biology and eLife, and was presented as a talk in leading computational neuroscience conferences such as Cosyne and Bernstein Conference. His work was featured in news outlets such as The Independent. His research addresses how the brain handles unavoidable uncertainty (e.g., from ambiguous visual scene) during sequential decision-making (e.g., wayfinding). It helps understand players’ behaviour and predict their uncertainty given a map (and hence difficulty). Since neurological patients often show specific impairments in such tasks, it may help earlier and more specific diagnosis of diseases. Yul is interested in predicting players’ behaviour, procedural generation of levels by predicting subjective uncertainty and fun, and using games for diagnosis of psychiatric and neurological diseases. yul.kang@qmul.ac.uk Email Mastodon https://www.yulkang.net/ Other links Website https://www.linkedin.com/in/yul-kang-9b11522b/ LinkedIn BlueSky https://github.com/yulkang Github Themes Creative Computing Game AI Immersive Technology Player Research - Previous Next

< Back Dr Catherine Flick iGGi Responsible Innovation Lead Supervisor Catherine Flick is a Reader in Computing and Social Responsibility at De Montfort University and has a particular interest in the ethics of emerging technologies (including video games). Her video game research is largely interdisciplinary and focused on the social and ethical impacts of and ethics in design of video games. Previous video game research and talk topics includes on Pokemon Go and mental health, design of moral decision making systems in Bioware games, the representation of chickens in video games, the philosophy of zombie games, desirability of lootboxes, serious games for the hearing impaired, etc. She regularly attends and speaks at PAX East, and has spoken on games and similarly weird things at various conferences and events internationally. She is also the responsible innovation lead for the IGGI programme, so has a particular interest in development of codes of ethics or ethical design principles for games, having worked on the updated ACM Code of Ethics and run EU funded projects that developed responsible innovation guidelines in the fields of healthcare IT, smart homes/smart health, cyber security, nanotechnology & biomedicine. She is particularly interested in students who are excited about the intersection of video games and society from a critical philosophical perspective, or from a social sciences perspective. Research themes: Ethical Game Design Games with a Purpose Player Experience Gamification Social/Ethical Impact of Games Diversity & Inclusion in Games Philosophy & Games catherine.flick@staffs.ac.uk Email https://mastodon.me.uk/@CatherineFlick Mastodon https://www.liedra.net Other links Website LinkedIn BlueSky https://github.com/liedra Github Themes Accessibility Applied Games Player Research - Previous Next

< Back Dr Dino Ratcliffe Queen Mary University of London iGGi Alum Teaching AI agents transferable skills for game playing My research focuses on the ability of an AI agent to be able to evaluate the various skills it would need to master a game, such as in an FPS (first person shooter) like doom. If the agent can learn to cluster actions that may split into strategies such as attacking enemies, gathering ammo/health and avoiding enemy fire this information could then be used in similar games. This information would also provide a base for being to evaluate players on a skill level, giving a much more granular view of their strengths and weaknesses in any of these games. This could then be used for better matchmaking in team games, placing players into teams whose skill sets complement each other. Other applications include being able to guide the player into situations that give them more experience in the areas they are weakest. Dino started a MSci in computer science at the University of Essex in 2011. During the next 4 years, he focused on modules that involved improving technical skills and Artificial Intelligence. He was the winner of the K.F Bowden Memorial prize in two separate years. Dino worked at the London startup Signal Media during the summer of 2014 and continued to work for them part time during my masters year. He graduated with a 1st class degree. Please note: Updating of profile text in progress Email Mastodon Other links Website LinkedIn BlueSky Github Featured Publication(s): Cross-lingual style transfer with conditional prior VAE and style loss Author's declaration Win or learn fast proximal policy optimisation Domain Adaptation for Deep Reinforcement Learning in Visually Distinct Games Clyde: A deep reinforcement learning doom playing agent Themes Game AI - 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. smurt001@gold.ac.uk Email Mastodon Other links Website LinkedIn BlueSky Github Themes Game AI - Previous Next

< Back Dr Nick Ballou Queen Mary University of London iGGi Alum Hi there! I’m a psychology and human-computer interaction researcher interested in two main topics: how games affect wellbeing, and how we can reform the research ecosystem to be more trustworthy and efficient (aka “open science” or “metascience”). I’m originally from the US, and have bachelor and master’s degrees in linguistics, a topic that prepared me well for social science research, but whose use is relegated to excitedly sharing language fun facts at this point. In my free time, I play tennis, cook and bake, read—and of course play games (mostly deckbuilders, roguelikes, and AAA RPGs). A description of Nick's research: Psychological need frustration—experiences of feeling controlled and coerced, failure and self-doubt, or loneliness and exclusion—is a promising framework for understanding how players engage with video games. Grounded in self-determination theory, one of the most robust psychological theories, need frustration might help explain how and why players (dis)engage with a game and how gameplay impacts well-being. To realize this aim, however, we’re missing key building blocks: 1) a better grasp on when and why need-frustrating situations arise during play; 2) a questionnaire that can assess how much need frustration people experience in games quantitatively; and 3) studies that combine data on need frustration with carefully tracked behavioral data over time, rather than relying on simple self-reports like “how much time did you spend playing video games last week?” My thesis attempts to address all of these one step at a time and is underpinned by a strong emphasis on open and transparent methods. Results so far are promising—contact me to hear more! nick@nickballou.com Email Mastodon https://www.nickballou.com Other links Website LinkedIn BlueSky Github Supervisors: Prof. Sebastian Deterding Dr David Zendle Dr Laurissa Tokarchuk Featured Publication(s): Reliving 10 years old: Descriptive Insights into Retro Gaming UKRN Local Network Lead Guidebook Claims for no evidence also need evidence From social media to artificial intelligence: improving research on digital harms in youth The Basic Needs in Games (BANG) Model of Video Game Play and Mental Health (PhD thesis) The Basic Needs in Games (BANG) Model of Video Games and Mental Health: Untangling the Positive and Negative Effects of Games with Better Science The Relationship Between Lockdowns and Video Game Playtime: Multilevel Time-Series Analysis Using Massive-Scale Data Telemetry Affective Uplift During Video Game Play: A Naturalistic Case Study No evidence that Chinese playtime mandates reduced heavy gaming in one segment of the video games industry A manifesto for more productive psychological games research Four grand challenges for video game effects scholars: How digital trace data can improve the way we study games Perceived value of video games, but not hours played, predicts mental well-being in adult Nintendo players Development of the Brief Open Research Survey (BORS) to measure awareness and uptake of Open Research practices The Basic Needs in Games Scale (BANGS): A new tool for investigating positive and negative video game experiences How does Juicy Game Feedback Motivate? Testing Curiosity, Competence, and Effectance Registered Report Evidence Suggests No Relationship Between Objectively Tracked Video Game Playtime and Well-Being Over 3 Months How do video games affect mental health? A narrative review of 13 proposed mechanisms Learnings from the case Maple Refugee: A dystopian story of free-to-play, probability, and gamer consumer activism. Four dilemmas for video game effects scholars: How digital trace data can improve the way we study games Cross-cultural patterns in mobile playtime: an analysis of 118 billion hours of human data Pinpointing the problem: Providing page numbers for citations as a crucial part of open science A large-scale study of changes to the quantity, quality, and distribution of video game play during the COVID-19 pandemic Reforms to improve reproducibility and quality must be coordinated across the research ecosystem: the view from the UKRN Local Network Leads ‘I Just Wanted to Get it Over and Done With’: A Grounded Theory of Psychological Need Frustration in Video Games A Manifesto for More Productive Psychological Games Research Understanding whether lockdowns lead to increases in the heaviness of gaming using massive-scale data telemetry: An analysis of 251 billion hours of playtime If everything is a loot box, nothing is: Response to Xiao et al. Awareness of and engagement with Open Research behaviours: Development of the Brief Open Research Survey (BORS) with the UK Reproducibility Network Do People Use Games to Compensate for Psychological Needs During Crises? A Mixed-Methods Study of Gaming During COVID-19 Lockdowns Self-Determination Theory in HCI: Shaping a Research Agenda Themes Game Data Player Research Previous Next

< Back Dr Cristiana Pacheco Queen Mary University of London iGGi Alum Cristiana is a researcher with a passion for game development. Her research explores how to assess believability in video games and model/develop human-like behaviour. In addition, her research investigates applying these techniques in general, rather than a single specific game. She finished her BSc in Computer Games in Essex, where she also worked as a research assistant for an autonomous car racing project. She then started her PhD at Queen Mary University of London focused on games believability. Since, she has completed her placement at Ninja Theory, where she collaborated with Microsoft Research in Project Paidia. This opportunity provided experience with both game development and research. As a PhD student in her last year, she is working on the modelling of players through gameplay data and how this can be used to develop more human-like AI. The goal is to combine her research concepts into agents that do not always play to win, but rather present a diverse set of behaviours. c.pacheco@qmul.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/cpache111/ LinkedIn BlueSky https://github.com/Cpache1 Github Supervisor(s): Prof. Richard Bartle Dr Laurissa Tokarchuk Dr Diego Pérez-Liébana Featured Publication(s): Believability Assessment and Modelling in Video Games Predictive models and monte carlo tree search: A pipeline for believable agents Discrete versus Ordinal Time-Continuous Believability Assessment Trace it like you believe it: Time-continuous believability prediction Studying believability assessment in racing games PAGAN for Character Believability Assessment Rolling Horizon Co-evolution in Two-player General Video Game Playing Themes Creative Computing - Previous Next

< Back Dr Gavin Kearney University of York Supervisor Dr Gavin Kearney is a highly experienced researcher, lecturer and content creator specialising in spatial audio and surround sound. He joined the University of York as Lecturer in Sound Design in January 2011 and was appointed Associate Professor in Audio and Music Technology in 2016. He has written over 60 research articles and patents on different facets of immersive and interactive audio, including real-time audio signal processing, Ambisonics, virtual and augmented reality and recording and audio post-production technique development. He has undertaken innovative projects in collaboration with Mercedes-Benz Grand Prix, BBC, Dolby, Huawei, Abbey Road and Google amongst others. With the latter, he helped define the Google spatial audio pipeline through development of the SADIE binaural filters and decoders used worldwide. He is also an active sound engineer and producer of immersive audio experiences, working to develop new techniques and workflows for immersive music production in collaboration with Abbey Road Studios. He is Vice-Chair of the AES Audio for Games Technical Committee and was Co-Chair of the 2019 AES Immersive and Interactive Audio Conference at York. Gavin is particularly interested in supervising students with an audio background who wish to explore the following areas relating to audio for games Intelligent sound design Virtual Acoustics Spatial Audio Binaural sound Audio for Virtual and Augmented Reality Immersive audio experiences for next gen mobile platforms Ambisonics and spherical acoustics Using audio to enhance player emotional state (as well as projects on health and well-being) Game Audio for therapy Accessibility through Game Audio gavin.kearney@york.ac.uk Email Mastodon https://www.audiolab.york.ac.uk Other links Website https://www.linkedin.com/in/gavin-p-kearney LinkedIn BlueSky Github Themes Accessibility Applied Games Game AI Game Audio - Previous Next

< Back Prof. Massimo Poesio Queen Mary University of London Supervisor Massimo Poesio is a cognitive scientist whose primary field is Computational Linguistics / Natural Language Processing. He is interested in the interdisciplinary study of language processing using evidence from computational modelling, corpora, psychological studies, and neuroscience; specific interests include computational models of anaphora resolution (coreference); the study of disagreement on language interpretation through the creation of large corpora containing multiple judgments (an area in which he pioneered the use of games-with-a-purpose with the development of Phrase Detectives, http://www.phrasedetectives.org ); the interpretation of verbal and non-verbal communication in interaction; and the study of conceptual knowledge using a combination of methods from human language technology and neuroscience. He has also been involved in a number of projects applying NLP methods to real life problems, such as detecting deception online, or identifying human rights violations reports in social media. He holds a European Research Council grant on identifying disagreements in language through Games-With-A-Purpose, DALI and is a co-founder of the open access journal Dialogue and Discourse . Using conversational agents in games Applying games to label data for AI Research themes: Game AI Game Design Games with a Purpose m.poesio@qmul.ac.uk Email Mastodon https://sites.google.com/view/massimo-poesio/ Other links Website LinkedIn BlueSky https://github.com/dali-ambiguity Github Themes Applied Games Game AI - Previous Next

< Back George Long Queen Mary University of London iGGi PG Researcher Available for placement George is an IGGI PhD student interested in AI assisted game design, particularly in how it can be used to assist in the creation and balancing of game mechanics. After graduating with a BSc in Computer Science at the University of Essex, he joined IGGI in 2021 to be able to research how Artificial Intelligence can be applied specifically to reduce the prevalence of Min-Maxing in Role-Playing Games. A description of George's research: My research focuses on the concepts of Min-Maxing and Meta in Role-Playing Games, and how we can use AI assisted game design to reduce their prevalence. Min-Maxing in Role-Playing Game refers to the idea of building a character in a Role-Playing Game by maximising their positive traits while minimising negative ones, often through exploiting game mechanics. This can cause optimal strategies to emerge which not only have the potential to upset the game balance, but when these strategies become prominent enough in the community to form a Meta, it can have wider consequences such as the shunning of players deemed not to be using optimal strategies, and loss of creative choice when building characters. There are two methods I am looking into to reduce the effectiveness of Min-Maxing. The first is using AI to discover these Min-Maxed strategies. Secondly, how AI can be used in the game balancing process to identify and modify the mechanics which enable these strategies. Currently, I am focusing on the first method, with my research looking into how we can measure the effectiveness of units in combat scenarios to identify which units could be considered unbalanced. g.e.m.long@qmul.ac.uk Email Mastodon http://www.longhouse.dev Other links Website https://www.linkedin.com/in/georgelonghouse/ LinkedIn BlueSky Github Supervisor(s): Dr Diego Pérez-Liébana Featured Publication(s): PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games Themes Design & Development Game AI Game Data - Previous Next

< Back Dr Joseph Walton-Rivers University of Essex iGGi Alum Controlling Non-player characters. (Industry placement at Visteon) Within games non-player characters help to sell the world and give meaning to the player's experiences. These characters in games are presently not very believable and often lack the ability to interact with each other in meaningful ways. This work is looking at creating socially capable, believable agents to populate the worlds of role playing games. These agents need to be able to cope with player's actions and be capable of acting independent in the world. Joseph studied computer science at the University of Essex, obtaining a first class degree. During his study there he received two awards for academic achievement. After graduation he worked in the IT team of a company with offices across the United Kingdom where he developed and maintained their IT systems. Since starting IGGI he has worked on research involving co-operative agents working together to solve shared goals. He has a keen interest in programming and the Free Software movement. During his free time he enjoys strategy and puzzle games including Prison Architect, the Shadowrun series and Galactic Civilization 2. Please note: Updating of profile text in progress Email Mastodon Other links Website LinkedIn BlueSky Github Featured Publication(s): Evaluating the Effects on Monte Carlo Tree Search of Predicting Co-operative Agent Behaviour An Exploratory Analysis of Student Experiences with Peer Evaluation in Group Game Development Projects Student Perspectives on the Purpose of Peer Evaluation During Group Game Development Projects The 2018 Hanabi competition Hexboard: A generic game framework for turn-based strategy games Fireworks agent competition Evaluating and Modelling Hanabi-Playing Agents Controlling co-incidental non-player characters Monte carlo tree search applied to co-operative problems Distributed Social Multi-Agent Negotiation Framework For Incomplete Information Games Themes Player Research - Previous Next

< Back Dr Mariana Lopez University of York Supervisor Dr Mariana Lopez is a researcher in the field of sound design. She works on two main fields: accessibility and heritage. Her work on accessibility focuses on how sound design can be used to create accessible experiences for film and television audiences with sight loss, providing an alternative to traditional Audio Description practices. She was the Principal Investigator of the project Enhancing Audio Description funded by the AHRC. In the field of heritage, she focuses on acoustical heritage, by exploring how acoustic measurement techniques, computer modelling and the recreation of soundscapes can help us understand the sonic experiences of our ancestors. She was the Principal Investigator of the British Academy-funded project – The Soundscapes of the York Mystery Plays. Related to these fields she is supervising and has supervised projects in the field of mental health in connection to the creative arts; interactive installations; serious gaming and its impact on sustainability; accessibility and gaming; and sound design in participatory theatre, among others. Research themes: Game Audio and Music Games with a Purpose Sound and accessibility Equality and social justice Acoustical heritage Sound installations mariana.lopez@york.ac.uk Email Mastodon https://marianajlopez.com/ Other links Website https://uk.linkedin.com/in/mariana-lopez-9a229096 LinkedIn BlueSky Github Themes Applied Games Game Audio - Previous Next

< Back Dr Adam Katona University of York iGGi Alum Adam did his MSc in mechatronics at Budapest University of Technology and Economics. After graduation, he spent two years working on automated driving at Robert Bosch GmbH, during which he got exposed to both the classical and the machine learning approach of creating intelligent agents. Evolutionary computation continues to surprise us by producing creative and efficient designs. However despite our best efforts, artificial evolution had not produced anything ascomplex and interesting as natural evolution. As our hardware is becoming faster and number of cores in our chips increase, the lack of computational power is becoming less of an excuse. It is starting to become more and more obvious that some fundamental component of natural evolution is missing from our simulations. One possible candidate is the evolution of evolvability. Evolution seems to produce organisms which are well suited for further evolution. The goal of my research is to find mechanisms which allows evolution to increase evolvability, and incorporate these in the design of more efficient neuroevolution algorithms.This research is in the intersection of evolutionary computation, evolutionary developmental biology and neural networks. mail.adamkatona@gmail.com Email Mastodon https://adamkatona.net/ Other links Website LinkedIn BlueSky https://github.com/adam-katon Github Featured Publication(s): Illuminating Game Space Using MAP-Elites for Assisting Video Game Design Complex computation from developmental priors Utilizing the Untapped Potential of Indirect Encoding for Neural Networks with Meta Learning Quality Evolvability ES: Evolving Individuals With a Distribution of Well Performing and Diverse Offspring Growing 3d artefacts and functional machines with neural cellular automata Time to die: Death prediction in dota 2 using deep learning Themes Game AI - Previous Next