Search Results

< Back ​ Dr Debbie Maxwell University of York iGGi Research Collaboration Coordinator Supervisor ​ ​ Debbie is a lecturer in User Experience Design and Interactive Media at the Department of Theatre, Film and Television. Her background spans computing, HCI and Design and she currently teaches user experience (UX) design and design methods and critical design on the BSc Interactive Media programme. Her research focuses on the roles of traditional storytelling and engagement in digital contexts. I’m interested in the ways that people interact with and reshape technology through stories, as both method and artefacts, and across media. She is particularly focuses on applying design and stories across health and wellbeing and environmental design drawing on speculative design processes and approaches. Debbie uses interdisciplinary approaches that draw on a range of fields including Human Computer Interaction (HCI), ethnography, interaction design, social anthropology, and service design. Her research always involves working with communities using participatory methods. She is particularly interested in supervising students with a design or HCI background on the following topics: design of applied games for environmental education or knowledge exchange design and application of serious games to mental health and wellbeing contexts design and application of serious games to outdoor spaces, particularly cultural heritage settings Research themes: Games with a Purpose User experience design Design methods and ethnography Speculative design ​ debbie.maxwell@york.ac.uk Email Mastodon Other links Website LinkedIn https://twitter.com/@deb_max Twitter Github ​ ​ Themes Applied Games Design & Development Player Research - Previous Next

< Back TAG: A tabletop games framework Link ​ Author(s) R Gaina, M Balla, A Dockhorn, R Montoliu Colas Abstract ​ More info TBA ​ Link

< 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 Mastodon Other links Website https://www.linkedin.com/in/robert-j-homewood-36906132/ LinkedIn https://twitter.com/@rob_homewood Twitter Github ​ ​ Themes Player Research - Previous Next

< Back ​ Ruizhe 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 https://twitter.com/RuizheYu Twitter Github Supervisor: Prof. Simon Lucas ​ Themes Game AI Game Data - Previous Next

< Back TileAttack Link ​ Author(s) C Madge Abstract ​ More info TBA ​ Link

< Back ​ Michelangelo Conserva Queen Mary University of London ​ iGGi PG Researcher ​ Available for placement Michelangelo Conserva is a second year PhD researcher studying principled exploration strategies in reinforcement learning. He is particularly interested in randomized exploration and, more generally, Bayesian methods for reinforcement learning. He holds a BSc in Statistics, Economics and Finance from Sapienza, University of Rome and an MSc in Computational Statistics and Machine learning from University College of London. A description of Michelangelo's research: As a PhD student at Queen Mary University of London, Michelangelo aims to leverage Bayesian models to develop principled algorithms for reinforcement learning in the context of function approximations. The main challenge lies in finding a balance between computational costs and optimality. Evaluating such balance requires careful evaluation, which is currently lacking in reinforcement learning. ​ m.conserva@qmul.ac.uk Email Mastodon https://michelangeloconserva.github.io/ Other links Website https://www.linkedin.com/in/michelangeloconserva/ LinkedIn https://twitter.com/Michelangelo755 Twitter https://github.com/MichelangeloConserva Github Supervisors: Prof. Simon Lucas Dr Paulo Rauber Featured Publication(s): What are you looking at? Team fight prediction through player camera Posterior Sampling for Deep Reinforcement Learning Hardness in Markov Decision Processes: Theory and Practice Recurrent Neural-Linear Posterior Sampling for Nonstationary Contextual Bandits The Graph Cut Kernel for Ranked Data Themes Game AI - Previous Next

< Back A Manifesto for More Productive Psychological Games Research Link ​ Author(s) N Ballou Abstract ​ More info TBA ​ Link

< Back Robust Imitation Learning for Automated Game Testing Link ​ Author(s) PV Amadori, T Bradley, R Spick, G Moss Abstract ​ More info TBA ​ Link

< Back Lateralization of impedance control in dynamic versus static bimanual tasks Link ​ Author(s) N Pena-Perez, J Eden, I Farkhatdinov, E Burdet, A Takagi Abstract ​ More info TBA ​ Link

< 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 Twitter Github ​ ​ Themes Game AI - Previous Next

< Back Not all fun and games: The design and evaluation of a game to increase intrinsic motivation in learning programming Link ​ Author(s) E Petrovskaya Abstract ​ More info TBA ​ Link

iGGi Partners We are excited to be collaborating with a number of industry partners. IGGI works with industry in some of the following ways: ​ Student Industry Knowledge Transfer - this can take many forms, from what looks like a traditional placement, to a short term consultancy, to an ongoing relationship between the student and their industry partner. Student Sponsorship - for some of our students, their relationship with their industry partner is reinforced by sponsorship from the company. This is an excellent demonstration of the strength of the commitment and the success of the collaborations. In Kind Contributions - IGGI industry partners can contribute by attending and/or featuring in our annual conference, offering their time to give talks and masterclasses for our students, or even taking part in our annual game jam! ​ There are many ways for our industry partners to work with IGGI. If you are interested in becoming involved, please do contact us so we can discuss what might be suitable for you. Ubisoft Massive Entertainment