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< Back Studying believability assessment in racing games Link Author(s) C Pacheco, L Tokarchuk, D Perez-Liebana Abstract More info TBA Link

< Back Synthesising Knocking Sound Effects Using Conditional WaveGAN Link Author(s) A Barahona-Rıos, S Pauletto Abstract More info TBA Link

< Back Cross-lingual style transfer with conditional prior VAE and style loss Link Author(s) D Ratcliffe, Y Wang, A Mansbridge, P Karanasou, A Moinet, M Cotescu Abstract More info TBA Link

< Back No Item Is an Island Entire of Itself: A Statistical Analysis of Individual Player Difference Questionnaires Link Author(s) N Hughes, P Cairns Abstract More info TBA Link

< Back Generative design in Minecraft: Chronicle challenge Link Author(s) C Salge, C Guckelsberger, MC Green, R Canaan, J Togelius 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. Digital Catapult

< Back Rolling Horizon NEAT for General Video Game Playing Link Author(s) D Perez-Liebana, MS Alam, RD Gaina 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

< Back Predicting game difficulty and engagement using AI players Link Author(s) S Roohi, C Guckelsberger, A Relas, H Heiskanen, J Takatalo, ... Abstract More info TBA Link

< Back A FAIR catalog of ontology-driven conceptual models Link Author(s) TP Sales, PPF Barcelos, CM Fonseca, IV Souza, E Romanenko, J Kritz ... Abstract More info TBA Link

< Back Strategy Games: The Components of A Worthy Opponent Link Author(s) D Gomme, R Bartle Abstract More info TBA Link

Machine Learning of Procedural Audio Theme Game Audio Project proposed & supervised by Joshua Reiss To discuss whether this project could become your PhD proposal please email: joshua.reiss@qmul.ac.uk < Back Machine Learning of Procedural Audio Project proposal abstract: Game sound design relies heavily on pre-recorded samples, but this approach is inflexible, repetitive and uncreative. An alternative is procedural audio, where sounds are created in real-time using software algorithms. But many procedural audio techniques are low quality, or tailored only to a narrow class of sounds. Machine learning from sample libraries to select, optimise and improve the procedural models, could be the key to transforming the industry and creating procedural auditory worlds. This work will build on recent high impact research from the team to investigate whether procedural audio can fully replace the use of pre-recorded sound effects. See https://nemisindo.com for examples of procedural sound effects. Supervisor: Joshua Reiss Based at: This project will be a collaboration with Nemesindo .