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< Back Redundancy Resolution in Trimanual vs. Bimanual Tracking Tasks Link Author(s) A Sanmartín-Senent, N Peña-Perez, E Burdet, J Eden Abstract More info TBA Link

< Back Distributed Social Multi-Agent Negotiation Framework For Incomplete Information Games Link Author(s) J Walton-Rivers, E Longford, D Gomme, R Bartle, M Gardner Abstract More info TBA Link

< Back Ms. Pac-Man Versus Ghost Team CIG 2016 Competition Link Author(s) PR Williams, D Perez-Liebana, SM Lucas Abstract More info TBA Link

< Back Better Dead than a Damsel: Gender Representation and Player Churn Link Author(s) Lauren Winter, Sarah Masters Abstract More info TBA Link

Understanding ongoing mental states using video games: applications to mental health research. Theme Game Data Project proposed & supervised by Alex Wade To discuss whether this project could become your PhD proposal please email: alex.wade@york.ac.uk < Back Understanding ongoing mental states using video games: applications to mental health research. Project proposal abstract: A player’s behaviour in a game is directly linked to their personality and gives detailed information on their decision making processes, showing how they approach risks, socialisation and problem solving. Analysing these behaviours may also provide information about mental health disorders and indicate how these change over time. Neuroimaging methods (EEG/MEG/fMRI) can be used to examine the neural responses and patterns of ongoing neuronal activity that occur while players are engaged in a game. By linking these data to modern theories of neural economics we can explore and potentially improve aspects of a player's decision making, such as: attention span, focus, risk taking and delayed reward. This PhD will use a combination of neuroscience and advanced data analysis methods to examine the link between video game play and the brain. We will use a combination of cutting-edge data analytic techniques applied to large, existing video game telemetry datasets and neuroimaging experiments designed to measure changes in ongoing mental states while people play simple video games. The PhD would suit a student with good data analytics skills and some experience in neuroscience. Supervisor: Alex Wade Based at:

Building Player Profiles in Mobile Monetisation: A Machine Learning Approach Theme Game Data Project proposed & supervised by David Zendle To discuss whether this project could become your PhD proposal please email: david.zendle@york.ac.uk < Back Building Player Profiles in Mobile Monetisation: A Machine Learning Approach Project proposal abstract: This project aims to use machine learning techniques to segment and profile mobile gamers in terms of their in-game spending. Estimates suggest that more than 2.6bn people play mobile games globally; that more than 80 billion mobile games are downloaded annually; and that mobile gaming accounts for almost $100bn in transactions every year. Despite the profitability of mobile gaming, little is known about how different kinds of players spend money in mobile games. Informal theories regarding specific differences in gaming are widely espoused: one influential model, for example, posits the existence of a small but profitable layer of heavily-involved 'whales', and much larger groups of smaller-spending 'dolphins' and 'minnows'. However, it is unclear whether this structure really does explain the monetisation of most games; and whether monetisation may vary between games; and between cultural contexts. In this project, we will take a data-driven approach, and apply a variety of machine learning techniques to large datasets of real player transactions. By both applying and developing algorithmic techniques for the analysis of such data, we will help build an understanding of how in-game spending may be profiled. This project would suit a machine learning specialist; a quantitative social scientist, or a data scientist wishing to do impactful work. It will be supervised by David Zendle, one of the world's leading experts on video game monetisation, and may involve one or more industrial partners who will share player data for the project. Supervisor: David Zendle Based at:

iGGi Admin Team (All) iGGi is a collaboration between Uni of York + Queen Mary Uni of London: the largest training programme worldwide for doing a PhD in digital games. The iGGi Admin Team Meet the iGGi Admin Team! The iGGi Admin Team consists of the two iGGi Managers (one per each active site), three iGGi Administrators, and one Administrative Assistant. Filter by Location iGGi Staff / Externals Filter by Role Title - All Staff - David Hull iGGi Manager University of York Manager of iGGi at UoY; local & global iGGi PGR admin; PGR recruitment; PGR Pastoral care; iGGi-global finances; iGGi community; events & training; EPSRC reporting Read More Shopna Begum iGGi Administrator Queen Mary University of London Administrator for iGGi at QMUL; general admin; PGR travel/room bookings; research expenses + purchases; progression monitoring; newsletter; local PGR support; events support Read More Susanne Binder iGGi Manager Queen Mary University of London Manager of iGGi at QMUL; local iGGi PGR admin; Industry liaison; PGR Pastoral care; iGGi-local finances; PR and website/social media, events & training; Supervisor community Read More Oliver Roughton iGGi Administrator University of York Administrator for iGGi at UoY; general admin; PGR + staff travel/room bookings; research expenses + purchases; PGR support Read More Helen Tilbrook iGGi Administrator University of York Administrator for iGGi at UoY; general admin; PGR + staff travel/room bookings; research expenses + purchases; PGR recruitment admin; progression monitoring; alumni; PGR support Read More Nicole Levermore iGGi Assistant - Alumni Liaison University of York Administrative Assistant for iGGi; Alumni Network curation + support; parental leave cover; iGGi case studies; SM support Read More

< Back iGGi QMUL is located at the heart of East London on Queen Mary, University of London's Whitechapel campus. iGGi QMUL is part of QMUL’s School of Electronic Engineering and Computer Science . While QMUL-based iGGi PGRs can belong to more than one research group, they all by default belong to the Game AI Group (GAIG) . The iGGi/GAIG office space is situated within the Digital Environment Research Institute (DERI) at Empire House, Whitechapel campus. How to reach the iGGi Offices at Empire House, Whitechapel The address for the iGGi office space is 2nd Floor Empire House DERI 67-75 New Road London, Whitechapel E1 1HH Whitechapel campus map Accesibility: Empire House access guide Arriving by Tube The Whitechapel campus is easily accessible via public transport, with the Whitechapel Underground station on London Underground's Elizabeth Line (purple on the Tube map), Hammersmith and City Line (pink on the Tube map), and District Line (green on the Tube map), just a seven minute walk away. When you exit the station, turn right and walk along Whitechapel Road until the next larger junction. Turn left into New Road. Empire House will be located to your right. Please use the Transport for London Journey Planner to help you plan your journey: https://tfl.gov.uk/plan-a-journey/ or their interactive maps showing Underground, Docklands Light Railway (DLR) and bus information Arriving by Bus The Whitechapel campus is based on Whitechapel Road, on the 25 and 205 bus routes, and Empire House is just off Whitechapel Road, on New Road. Cycling/Walking If you are travelling by bike or walking, please use the postcode above and the campus map to help you navigate to the venue. Bike storage facilities can be found in the Empire House Basement. Arriving by car For both our Mile End and Whitechapel campuses, car parking for visitors is not offered due to our central location. Local parking restrictions also apply on weekdays and weekends.We therefore strongly recommend you use one of the alternative transport methods listed above. If you do need to drive to campus, QMUL open day published a list of offsite parking options within easy reach of Whitechapel, including park and ride options. If you are a blue badge holder and require parking on site, please see the university's related information pages . Queen Mary University of London (QMUL) iGGi QMUL Gallery Map depicting QMUL Mile End campus & the iGGi Con 2023 venue location iGGi Con 2023 venue: The Graduate Centre (Mile End campus, QMUL), viewed from Bancroft Road iGGi Con 2023 venue: Ground floor entrance of the Graduate Centre - Mile End campus, QMUL Mile End campus with the Graduate Centre on the left Birds eye view of Mile End campus, QMUL Map depicting QMUL Whitechapel campus with Empire House where all of the iGGi Office space is located Empire House Basement, QMUL (Whitechapel) iGGi office space, Empire House, QMUL (Whitechapel campus) The Blizard Building opposite Empire House, Whitechapel campus (QMUL) Previous Next

< Back Opening the World of Contextually-Specific Player Experiences Link Author(s) NGJ Hughes, P Cairns 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. Rebellion

< Back Approximating the Manifold Structure of Attributed Incentive Salience from Large-scale Behavioural Data: A Representation Learning Approach Based on Artificial Neural Networks Link Author(s) V Bonometti, MJ Ruiz, A Drachen, A Wade Abstract More info TBA Link

< Back Exploring Minecraft Settlement Generators with Generative Shift Analysis Link Author(s) Jean-Baptiste Hervé, Oliver Withington, Marion Hervé, Laurissa Tokarchuk, Christoph Salge Abstract More info TBA Link