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< Back Registered Report Evidence Suggests No Relationship Between Objectively Tracked Video Game Playtime and Well-Being Over 3 Months Link ​ Author(s) N Ballou, CJR Sewall, J Ratcliffe, D Zendle, L Tokarchuk, S Deterding Abstract ​ More info TBA ​ Link

< Back ​ Callum Deery University of York ​ iGGi Alum ​ ​ Callum is a researcher and game developer investigating how real-time player experience measurement can be used to drive adaptive games. Aiming to embed player experience questionnaires into games in a way that doesn’t break immersion and presence, his PhD is focussed on leveraging the wide range of existing player experience questionnaires to improve games ability to adapt to players. This will involve exploring the states of immersion and presence: What is necessary to maintain them? What experiences can players reflect on without breaking immersion? How do we embed a questionnaire into an in-development game without disrupting the player experience? ​ callum.deery@gmail.com Email Mastodon https://cfdj.itch.io/ Other links Website LinkedIn https://twitter.com/CallumDeery2 Twitter Github Supervisors: Dr James Walker Dr Anna Bramwell-Dicks ​ Themes Accessibility Design & Development Player Research - Previous Next

< Back ​ Yizhao Jin Queen Mary University of London ​ iGGi PG Researcher ​ ​ Currently a student at Queen Mary University of London (QMUL), I have delved deep into the realms of artificial intelligence and game design. With a passion for understanding the complexities behind real-time strategy (RTS) games and their dynamic, unpredictable nature, I have committed myself to contribute novel insights to this domain. Research: My primary research area is Hierarchical Reinforcement Learning (HRL) for Real-Time Strategy (RTS) games. RTS games, known for their intricate mechanics and vast decision spaces, present a formidable challenge for traditional AI approaches. By employing HRL, I aim to develop agents that can not only understand the multi-layered tactics and strategies of these games but also learn to adapt to ever-changing game scenarios efficiently. The main objectives of my research are: Better Generalization: To create agents that can seamlessly transition between different RTS games or various maps within the same game without extensive retraining. This involves understanding common strategic threads across multiple game domains. Efficient Training: RTS games are inherently time-consuming due to their vast decision spaces and prolonged gameplay. My research seeks ways to optimize the training process, ensuring that AI agents can learn faster and with fewer computational resources. ​ acw596@qmul.ac.uk Email Mastodon Other links Website LinkedIn Twitter https://github.com/decatt Github Supervisors: Prof. Greg Slabaugh Prof. Simon Lucas ​ Themes Game AI Previous Next

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. University of Applied Arts Vienna

< Back PyTAG: Tabletop Games for Multi-Agent Reinforcement Learning Link ​ Author(s) M Balla, GEM Long, J Goodman, RD Gaina, D Perez-Liebana Abstract ​ More info TBA ​ Link

< Back ​ Alan Pedrassoli Chitayat University of York ​ iGGi PG Researcher ​ Available for post-PhD position Alan is a researcher that focuses on audience experience within esport broadcast. His Machine Learning background allows him to extract complex patterns from game and game related data in order to derive meaningful insights that can be utilised in broadcast. Having worked in the esport industry, both as a software engineer as well as researcher, Alan has experience with both technical and research problems. His research aims to explore the factors that improve the audience experience within esports. This is catered to esport broadcast of all levels, from highly produced professional tournaments to regular streams by content creators and it could be in the form of: Measuring and representing different forms of audience engagement. Exploring the different ways to visualise and utilise Machine Learning to enhance and integrate existing broadcast pipelines. Investigating how community-led narratives can be generated through data. ​ alan.pchitayat@york.ac.uk Email https://linktr.ee/alanpchitayat Mastodon https://alanpchitayat.com/ Other links Website https://www.linkedin.com/in/alan-pchitayat/ LinkedIn Twitter Github Supervisors: Dr James Walker Prof. Anders Drachen Featured Publication(s): How Could They Win? An Exploration of Win Condition for Esports Narratives Applying and Visualising Complex Models in Esport Broadcast Coverage From Passive Viewer to Active Fan: Towards the Design and Large-Scale Evaluation of Interactive Audience Experiences in Esports and Beyond Beyond the Meta: Leveraging Game Design Parameters for Patch-Agnostic Esport Analitics Data-Driven Audience Experiences in Esports Metagaming and metagames in Esports What are you looking at? Team fight prediction through player camera Echo Suite of Software (Showcase Brochure) Automatic Generation of Text for Match Recaps using Esport Caster Commentaries WARDS: Modelling the Worth of Vision in MOBA's DAX: Data-Driven Audience Experiences in Esports Themes Design & Development Esports Game Data - 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 https://twitter.com/@Mariana_J_Lopez Twitter Github ​ ​ Themes Applied Games Game Audio - Previous Next

< Back Computational models for the analysis and synthesis of graffiti tag strokes Link ​ Author(s) D Berio, FF Leymarie Abstract ​ More info TBA ​ Link

< Back Considerations and Concerns of Professional Game Composers Regarding Artificially Intelligent Music Technology Link ​ Author(s) K Worrall, T Collins 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. Valkyrie

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. BiG

< Back Different rules for binocular combination of luminance flicker in cortical and subcortical pathways Link ​ Author(s) FG Segala, A Bruno, MT Aung, AR Wade, DH Baker Abstract ​ More info TBA ​ Link