Search Results

< Back Ruizhe "Jay" Yu Xia Queen Mary University of London iGGi PG Researcher Available for placement Ruizhe has bachelor degrees in Mathematics and Physics and a master's degree in Artificial Intelligence. After a short time as a consultant he decided to pursue research into what got him into AI in the first place: game agents. He enjoys games of all kinds, but strategy and RPG games occupy a sizeable portion of his collection. AI agents that perform with superhuman skill in increasingly complex games have appeared in recent years, but these agents are not always useful to game developers. Players within a game exhibit significant variance in their skill levels and play styles. Therefore, game agents with similar variance would better represent the player base. The research Ruizhe proposes will focus on three areas: measuring skill and play styles, developing game agents that mimic a range of human play styles and skill levels, and making these agents human-like. Upon successful completion, this research will improve the game development process via automated playtesting and will enable the development of AI agents that are more engaging and interactive. r.yuxia@qmul.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/ruizheyuxia/ LinkedIn BlueSky Github Supervisor: Prof. Simon Lucas Dr Jeremy Gow Themes Game AI Game Data - Previous Next

< Back Dr Nathan Hughes University of York iGGi Alum Nathan Hughes is a player experience researcher who focuses on how player make choices within games. Specifically, the work explores open world games such as Skyrim and the Witcher 3, as these games allow players a vast amount of choice with little restrictions on how and when these are made. However, little research has considered these choices, so little is known about how players experience choice in open world games. Therefore, research questions for this work include; why do players choose not to pursue the main quest? What do players choose to do instead? When and how do they make this decision? His background is in psychology, and so asks these questions from a psychological perspective. The aim is to uncover how the process of choosing unfolds, and how this is influenced. In turn, this may allow reflections on how the decision-making process operates - by analysing choices within open world games, a more controlled (but still intrinsically motivating) setting can be studied. ngjhughes@gmail.com Email Mastodon https://faethfulexplorations.wordpress.com Other links Website https://www.linkedin.com/in/nathan-hughes-1035b611b/ LinkedIn BlueSky Github Supervisor Prof. Paul Cairns Featured Publication(s): Clinicians Risk Becoming "Liability Sinks" for Artificial Intelligence Understanding specific gaming experiences: the case of open world games The need for the human-centred explanation for ML-based clinical decision support systems Growing Together: An Analysis of Measurement Transparency Across 15 Years of Player Motivation Questionnaires Contextual design requirements for decision-support tools involved in weaning patients from mechanical ventilation in intensive care units Growing together: An analysis of measurement transparency across 15 years of player motivation questionnaires Opening the World of Contextually-Specific Player Experiences No Item Is an Island Entire of Itself: A Statistical Analysis of Individual Player Difference Questionnaires Ethereum Crypto-Games: Mechanics, Prevalence, and Gambling Similarities Themes Player Research - Previous Next

< Back Yu-Jhen Hsu Queen Mary University of London iGGi Alum I have always been interested in automation specifically within strategy games, starting from civilization 5. I have a background in Artificial Intelligence with a Master of Science degree from Queen Mary, University of London, with a focus on Game AI, Computer Vision and Machine Learning/Deep Learning. My research interests involve Game AI improvement in real-time turned-based games with the help of data science techniques. A description of Yu-Jhen's research: This project has two goals. Firstly, to improve the performance of MCTS (Monte Carlo Search Tree) implementation. Secondly, the goal is focused on building an AI agent that is able to win the game but also provide feedback information/data about it’s decisions to the players and designers. In order to achieve the goal, the plan of the project is to use different data science skills to enable the game AI agent to understand the utility of different actions and decrease the time needed for making decisions. The data collected can also help the game AI agent explain it’s behaviors, hence provided useful information/data for its users and designers. y.hsu@qmul.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/yujhenhsu/ LinkedIn BlueSky Github Supervisors: Dr Diego Pérez-Liébana Dr Raluca Gaina Featured Publication(s): Why Choose You?-Exploring Attitudes Towards Starter Pokémon Tribes: a new turn-based strategy game for AI research MCTS Pruning in Turn-Based Strategy Games. Themes Game AI Game Data - Previous Next

< Back Guilherme Matos de Faria University of York iGGi Alum I am a Portuguese student with a background in Artificial Intelligence. In 2016 I started attending video game tournaments and learned to analyse my matches and improve from it. When I did my masters in AI, I noticed that I could join my professional skills and my hobbies together to create something relevant to AI and competitive gaming. A description of James' research: I am looking to better understand which actions and decisions have the biggest impact on the outcome of a game. Currently, I am particularly focused on competitive turn based card games. What are the best players doing to win? How can players adapt to improve their chances of success? These are the questions I am hoping to help answer, giving players a better understanding of the game and how to improve. Please note: Updating of profile text in progress Email Mastodon Other links Website LinkedIn BlueSky Github Themes Game AI - Previous Next

< Back Prof. Alex Wade University of York Supervisor Alex Wade is a psychologist working in the field of human cognitive neuroscience. He uses a combination of structural and functional brain imaging, electrophysiology, psychophysics and big data analysis to ask how we see, solve problems and make decisions. His most recent work in the domain of video games focuses on what we can learn about global cognitive health and player personality from the analysis of large MOBA datasets in collaboration with Riot games (League of Legends). He is particularly interested in supervising students with a psychology or neuroscience background in the areas of: Using commercial video games to measure cognition and personality How the brain responds to solo- and group gameplay Can we use video games to monitor and modify real-world cognition, behaviour and mental health Research themes: Game Analytics Games with a Purpose Computational Creativity E-Sports Player Experience The neuroscience of gaming alex.wade@york.ac.uk Email Mastodon https://www.york.ac.uk/psychology/staff/academicstaff/alex-wade/ Other links Website LinkedIn BlueSky Github Themes Applied Games Creative Computing Esports Game Data Player Research - 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 Remo Sasso Queen Mary University of London iGGi PG Researcher I hold a BSc and MSc in Artificial Intelligence at the University of Groningen (NL) and am currently a PhD student at the Queen Mary University of London under the supervision of Paulo Rauber. In addition to my academic work, I have worked as a Machine Learning engineer, and am currently the Head of AI at xDNA, an AI/Cybersecurity-based start-up from the Netherlands. Here I'm leading the initiative Project Aletheia, where we develop AI-driven tools to optimize the workflow of professional fact-checkers, with the overarching goal of ensuring information integrity in the world. Foundation World Models and Foundation Agents for Reinforcement Learning My research focuses on developing reinforcement learning algorithms that are both scalable and sample-efficient through Bayesian methods and model-based approaches, recently with a particular emphasis on Large Language Models (LLMs). My previous research focused on principled, efficient and scalable exploration algorithms for reinforcement learning, e.g. Poster Sampling for Deep Reinforcement Learning (ICML 2023), where we developed a reinforcement learning algorithm that can be considered state-of-the-art in Atari games. Currently I'm particularly interested in the integration of LLMs in the reinforcement learning framework, both as decision-making agents and simulators. My current research, called "Foundation World Models and Foundation Agents for Reinforcement Learning" investigates this integration in-depth and shows that large models show significant potential in various reinforcement learning tasks, ranging from decision-making in stochastic environments to serving as world models. r.sasso@qmul.ac.uk Email https://remosasso.github.io/ Mastodon Other links Website https://www.linkedin.com/in/remo-sasso-b9837a1ba/ LinkedIn BlueSky https://github.com/remosasso Github Supervisor: Dr Paulo Rauber Featured Publication(s): VDSC: Enhancing Exploration Timing with Value Discrepancy and State Counts Making Connections: Neurodevelopmental Changes in Brain Connectivity after Adverse Experiences in Early Adolescence Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning Simultaneous multi-view object recognition and grasping in open-ended domains Posterior Sampling for Deep Reinforcement Learning Themes Game AI - Previous Next

< Back Pilar Zhang Qiu Queen Mary University of London iGGi Alum Pilar is a researcher with a background in Design Engineering. She has a keen interest in user experience and interaction, wearables and the use of cyber-physical systems in the medical field. Her PhD centres around the creation of play assessments for neuromotor conditions in children with cerebral palsy. This gravitates around the idea that better and more objective clinical data can be obtained through gamification of common assessments. Please note: Updating of profile text in progress Email Mastodon https://www.pilarzhangqiu.com/ Other links Website https://www.linkedin.com/in/pilar-zhang-qiu/ LinkedIn BlueSky https://github.com/pili-zhangqiu Github Themes Applied Games - 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 Mastodon Other links Website LinkedIn BlueSky Github Themes Game AI - Previous Next

< Back Doruk Balcı University of York iGGi PG Researcher Available for placement I am a game maker interested in the relationship between player creativity and game design. My work is centered around the transformative capabilities of players to invent their own metagames and play-practices, and how to support this through game design. My other interests include: drawing, literature, making zines and browser games, and playing with tools I don’t really understand. Designing for Appropriative Play How do we make games which we want to be messed with, changed fundamentally beyond our expectations in play? How do we make up rules that are intended to be bent, changed or broken? Why would we want that? Play practices that transform structures, subvert expectations and re-define their contexts are celebrated in many aspects of culture and can lead to personal and meaningful experiences. Yet research on this topic from a game design perspective has been scarce. In my project, I am exploring how we can design game systems that invite players to assume ownership of their play-practices through exploring alternative paradigms of game design. doruk.balci@york.ac.uk Email Mastodon https://fuzul.itch.io Other links Website https://www.linkedin.com/in/doruk-balc%C4%B1-19749a151 LinkedIn https://bsky.app/profile/dorukb.bsky.social BlueSky Github Supervisor: Dr Jo Iacovides Themes Design & Development Player Research - Previous Next

< Back Andrew Martin Queen Mary University of London iGGi Alum Applications in game development for programming language theory and AI Modern game development is highly iterative. Iteration is usually discussed in terms of a team completing design iterations, but can also be considered at the level of an individual developer attempting to complete a task, or experimenting with some ideas. At this level, the feedback loop provided by the tool becomes critical. Programming environments in particular often have a very poor feedback loop. Programming feedback can be thought of in terms of how quickly and seamlessly the user is able to observe the results of their work. This process is usually plagued with manual tasks and long pauses. It is common that a user will need to recompile, relaunch their program, and then manually recreate whatever state is required to observe the behaviour that they are working on. Frameworks like Elm, React and Vuejs are establishing a new norm of automatic hot-reloading with state preservation. These systems represent a branch of programming language research that is strongly focused on developer experience. In order to improve upon this work for game development, we must overcome the unique challenges that game development entails. Although the systems mentioned are all quite recent, there is a rich vein of research to draw on, which can be traced through dataflow programming, Smalltalk, Erlang, functional-reactive programming, Lisp and more. Predictive completions are considered by many to be a natural next-step in the evolution of live programming environments. An AI programming assistant would propose program fragments as completions or alternatives. The agent may seek to anticipate the user’s intent, or to provide creative suggestions. There is much relevant research in the fields of program synthesis, inductive logic programming, machine learning and genetic programming. One significant problem is how to smoothly and safely integrate a system like this into the user’s workflow. Many of the properties useful for safely enabling live programming features, such as isolation of side-effects, will also permit an AI agent to safely generate and execute code. Andy graduated from Imperial College London with an MEng in Computing in 2011. Following this he worked on game engine tools and technology at a startup called Fen Research, and then as a senior developer at a software consulting firm called LShift. In 2016 he spent six months working as a Research Associate in the Computational Creativity group at Goldsmiths, before starting his PhD. Please note: Updating of profile text in progress Email Mastodon Other links Website LinkedIn BlueSky Github Themes Game AI - Previous Next

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