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  • Efficient evolutionary methods for game agent optimisation: Model-based is best

    < Back Efficient evolutionary methods for game agent optimisation: Model-based is best Link Author(s) SM Lucas, J Liu, I Bravi, RD Gaina, J Woodward, V Volz, D Perez-Liebana Abstract More info TBA Link

  • The University of Sheffield

    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. The University of Sheffield

  • Evolving a designer-balanced neural network for Ms PacMan

    < Back Evolving a designer-balanced neural network for Ms PacMan Link Author(s) M Morosan, R Poli Abstract More info TBA Link

  • Procedural Generation using Spatial GANs for Region-Specific Learning of Elevation Data

    < Back Procedural Generation using Spatial GANs for Region-Specific Learning of Elevation Data Link Author(s) RJ Spick, P Cowling, JA Walker Abstract More info TBA Link

  • Time to die 2: Improved in-game death prediction in dota 2

    < Back Time to die 2: Improved in-game death prediction in dota 2 Link Author(s) C Ringer, S Missaoui, VJ Hodge, AP Chitayat, A Kokkinakis, S Patra, ... Abstract More info TBA Link

  • Analysis of statistical forward planning methods in Pommerman

    < Back Analysis of statistical forward planning methods in Pommerman Link Author(s) D Perez-Liebana, RD Gaina, O Drageset, E Ilhan, M Balla, SM Lucas Abstract More info TBA Link

  • Reforms to improve reproducibility and quality must be coordinated across the research ecosystem: the view from the UKRN Local Network Leads

    < Back Reforms to improve reproducibility and quality must be coordinated across the research ecosystem: the view from the UKRN Local Network Leads Link Author(s) SLK Stewart, CR Pennington, GR da Silva, N Ballou, J Butler, Z Dienes, ... Abstract More info TBA Link

  • Swords Narrative

    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. Swords Narrative

  • Collaborative creativity with Monte-Carlo Tree Search and Convolutional Neural Networks

    < Back Collaborative creativity with Monte-Carlo Tree Search and Convolutional Neural Networks Link Author(s) M Akten, M Grierson Abstract More info TBA Link

  • Play Style Identification Using Low-Level Representations of Play Traces in MicroRTS

    < Back Play Style Identification Using Low-Level Representations of Play Traces in MicroRTS Link Author(s) R Yu Xia, J Gow, S Lucas Abstract More info TBA Link

  • EReLELA: Exploration in Reinforcement Learning via Emergent Language Abstractions

    < Back EReLELA: Exploration in Reinforcement Learning via Emergent Language Abstractions Link Author(s) KY Denamganai, T Bradley, PV Amadori, S Missaoui, G Moss, JA Walker Abstract More info TBA Link

  • Human VR

    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. Human VR

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The EPSRC Centre for Doctoral Training in Intelligent Games and Game Intelligence (iGGi) is a leading PhD research programme aimed at the Games and Creative Industries.

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