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< Back How does machine learning affect diversity in evolutionary search? Procedural content generation of video games levels has greatly benefited from machine learning. In such complex domains, generative models can provide representation spaces for evolutionary search. But how expressive are such learned models? How many different levels would they be able to produce? A new paper, co-authored by IGGI PhD researcher Sebastian Berns and Professor Simon Colton, looks at the limitations of generative models in the context of multi-solution optimisation. The work will be presented at the Genetic and Evolutionary Computation Conference (GECCO) and is nominated for a best paper award . The study shows that quality diversity (QD) search in the latent space of a variational auto-encoder yields a solution set of lower diversity than in a manually-defined genetic parameter space. The authors find that learned latent spaces are useful for the comparison of artefacts and recommend their use for distance and similarity estimation. However, whenever a parametric search space is obtainable, it should be preferred over a learned representation space as it produces a higher diversity of solutions. Alexander Hagg, Sebastian Berns, Alexander Asteroth, Simon Colton & Thomas Bäck. (2021). Expressivity of Parameterized and Data-driven Representations in Quality Diversity Search. In Proceedings of the Genetic and Evolutionary Computation Conference. Pre-print available on arXiv https://arxiv.org/abs/2105.04247Accompanying code repository available on Github https://github.com/alexander-hagg/ExpressivityGECCO2021 ​ Previous 27 Jun 2021 Next

< Back Learning to see: you are what you see Link ​ Author(s) M Akten, R Fiebrink, M Grierson Abstract ​ More info TBA ​ Link

< Back ​ Helen Tilbrook University of York iGGi Administrator iGGi Admin ​ ​ iGGi Administrator at York ​ helen.tilbrook@york.ac.uk Email Mastodon Other links Website LinkedIn Twitter Github ​ ​ Themes - Previous Next

< Back ​ Dr Pengcheng Liu Queen Mary University of London ​ Supervisor ​ ​ Dr Pengcheng Liu is a Lecturer (Assistant Professor) at the Department of Computer Science, University of York, UK. He is an internationally leading expert in robotics, Artificial Intelligence and human-machine interaction. He has been leading and involving in several research projects, including EPSRC, Innovate UK, Horizon 2020, Erasmus Mundus, FP7-PEOPLE, HEIF, NHS I4I, NSFC, etc. Several of his research works were published on top-tier journals and leading conferences in the fields of robotics and AI. Before joining York, he has held several academic positions including a Senior Lecturer at Cardiff School of Technologies, Cardiff Metropolitan University, UK, a joint Research Fellowship at Lincoln Centre for Autonomous Systems (LCAS) and Lincoln Institute of Agri-Food Technology (LIAT), University of Lincoln, UK, a Research Assistant and a Teaching Assistant at Bournemouth University, UK. I also held academic positions as a Visiting Fellow at Institute of Automation, Chinese Academy of Sciences, China and Shanghai Jiao Tong University, China. Dr Liu is a Member of IEEE, IEEE Robotics and Automation Society (RAS), IEEE Systems, Man and Cybernetics Society (SMC), IEEE Control Systems Society (CSS) and IFAC. He is member of IEEE Technical Committees (TC) on Bio Robotics, Soft Robotics, Robot Learning, and Safety, Security and Rescue Robotics. He has published over 60 journal and conference papers. Dr Liu serves as an Associate Editor for IEEE Access and PeerJ Computer Science. He received the Global Peer Review Awards from Web of Science in 2019, and the Outstanding Contribution Awards from Elsevier in 2017. He was selected as regular Fundings/Grants reviewer for EPSRC, NIHR and NSFC. Dr Liu’s research interest relevant to CDT IGGI include applied games for healthcare and rehabilitation applications, as well as using mixed reality and machine learning for human-machine interactions. He is particularly interested in supervising students with a design, HCI, computer science or behavioural sciences background on the following topics: applied games for healthcare and rehabilitation design for adaptive mixed reality system for physical therapy and neurological rehabilitation design for physical and cognitive behaviour change learning for human intention prediction analysis of mixed reality rehabilitation system with biological signals (EEG, sEMG) ​ pengcheng.liu@york.ac.uk Email Mastodon https://sites.google.com/view/pliu Other links Website https://www.linkedin.com/in/pengcheng-liu-12703288/ LinkedIn Twitter Github ​ ​ Themes Applied Games Game AI Immersive Technology - Previous Next

< Back On State Representations and Behavioural Modelling Methods in Reinforcement Learning Link ​ Author(s) H Siljebrat Abstract ​ More info TBA ​ Link

< Back Visualising Generative Spaces Using Convolutional Neural Network Embeddings Link ​ Author(s) O Withington, L Tokarchuk 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. Stainless Games Limited

< Back A discussion of the use of virtual reality for training healthcare practitioners to recognize child protection issues Link ​ Author(s) O Drewett, G Hann, M Gillies, C Sher, S Delacroix, X Pan, T Collingwoode-Williams, ... 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

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. ESL UK

< Back ​ Zoë O’Shea Goldsmiths ​ iGGi PG Researcher ​ ​ Zoë O’Shea is an Irish freelance games designer and artist, working on her thesis in game design and player psychology. Her previous qualifications include 3D Generalism, and an MA in Digital Game Design and Theory. She is endlessly curious about the meaning and value that technology can bring to the world, exploring the human experience as a core principle of her work. She firmly believes in the importance of creating a more joyful and inclusive world. Zoë has previously worked with a range of clients and companies including A Brave Plan, Surgent Studios, Transport for London (TfL) and LEGO. In 2019, Zoë was awarded a Digital Fellowship from the Royal Shakespeare Company (RSC) in collaboration with Magic Leap. Zoë worked with other creatives for a year to explore the future of theatre and Spatial Computing (Mixed Reality). The programme completed in Feb 2020, through the generous support of Magic Leap, the RSC, their technologists, industry partners, i2 Media Research and the University of Portsmouth. Currently, Zoë is working on completing her thesis while offering consultancy services for games and start-ups looking to expand their knowledge in soft aesthetics, tend & befriend game design and immersive technology. A description of Zoë's research: Tend & Befriend: A New Perspective on Player Psychology in Digital Games Many are familiar with the term "fight-or-flight" - a stress response activated when animals come into conflict with a stressor or threat. Less commonly known is "tend & befriend", an alternative theory of stress response which suggests that engaging with tending and affiliative behaviours under duress can soothe and protect us. This thesis investigates this phenomenon in digital games, with a focus on empirical data and design. Results demonstrate a consistent niche in the games industry for "tend & befriend" centric design and the value in synthesising previous design frameworks to create a intentional and polished experience for players. ​ z.oshea@gold.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/meowmentai/ LinkedIn https://twitter.com/meowmentai Twitter Github Supervisor(s): Prof. Richard Bartle Featured Publication(s): The impact of self-representation and consistency in collaborative virtual environments Themes Design & Development Immersive Technology Player Research - Previous Next

< Back ​ Florence Smith Nicholls Queen Mary University of London ​ iGGi PG Researcher ​ ​ Florence Smith Nicholls is a game AI PhD researcher based in London. They worked as an archaeologist and heritage consultant for 7 years prior to their doctoral research. Building on their heritage background, they have contributed to the field of archaeogaming through experimenting with archaeological approaches to titles such as Elden Ring and Nier: Automata. They coined the term “generative archaeology games” for their doctoral project on the archaeological recording of procedurally generated content. Their work is motivated by contributing to wider research on games preservation, ethics of game AI and procedural narrative. They are also interested in queer approaches to games, having written about queerness and documenting glitches. They are also a freelance narrative designer, previously a member of the writers' room at the indie studio Die Gute Fabrik. During a placement at the British Library they worked on enhanced curation methods for narrative mobile apps, and were commissioned to produce gameplay footage for the Library's Digital Storytelling exhibition. ​ f.c.smithnicholls@qmul.ac.uk Email http://florencesmithnicholls.itch.io/ Mastodon https://florencesmithnicholls.com/ Other links Website LinkedIn https://twitter.com/florencesn Twitter Github Supervisor(s): Dr Mike Cook Dr Laurissa Tokarchuk ​ Themes Creative Computing Design & Development Game AI Game Data - Previous Next