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< Back MultiTree MCTS in Tabletop Games Link ​ Author(s) J Goodman, D Perez-Liebana, S Lucas Abstract ​ More info TBA ​ Link

< Back ​ Joshua Kritz Queen Mary University of London ​ iGGi PG Researcher ​ Available for placement Graduated in Applied Mathematics in computer science, however my love for games pushed me to dedicate myself for studying them. This led me to brave many areas of knowledge, such as: psychology, design, education, production and entrepreneurship. My work as a teacher allowed me develop many of these skills in practice, besides invoking a new perspective about the world. On a personal level, I love new experiences that can teach me new knowledge and, most important, I am very open minded and easy to talk to! I believe discussion leads to enlightenment. A description of Joshua's research: Card games, in particular Trading Card Games (TCGs) thrive on using the synergy between the cards to create emergent and interesting gameplay. However, these games usually have hundreds of different cards to create such rich experience, with some older TCGs featuring thousands of different cards. With such a huge amount of different cards playtesting these games present a big challenge. In example a new set of Magic the Gathering takes over 3 years of development to be fully designed. But even considering simpler exemplars like Dominion or Assencion can be difficult to balance, and both games are known to need a few expansions of experience to indeed provide a well balanced experience. One way to make this task faster and easier is to use automated agents to playtest the game exhaustively and provide much needed data. Whilst this would assist card game development, it is not used in practice, the playtesting of card games is still completely done by players. Even with systematic playtesting there is a limit of how much of the possibilities humans can test. However, implementing playtesting of card games have two big challenges, which are the main reason it has not been implemented in practice yet. First: Automated agents are not great when playing a game with too many variables (different cards) Second: The possible combinations of cards used in a deck or set of a single game is huge. My research aim to address the second issue by using a theory of synergy between cards to reduce the search space necessary to properly evaluate a card game. ​ j.s.kritz@qmul.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/joshua-kritz-38808379/ LinkedIn Twitter Github Supervisor: Dr Raluca Gaina ​ Themes Applied Games Design & Development Game AI - Previous Next

< Back AI and Wargaming Link ​ Author(s) J Goodman, S Risi, S Lucas Abstract ​ More info TBA ​ Link

< Back ​ Dr Poonam Yadav University of York ​ Supervisor ​ ​ Dr Yadav research is focused on making the Internet of Things (IoT) and edge computing-based distributed systems resilient, reliable, and robust. This is an interdisciplinary research area that requires expertise in system design and integration along with knowledge of sensor systems, wireless networking, and domain and contextual understanding. To achieve resilience and reliability in the area of resource constraints and distributed systems, I focus on coordination and collaboration using interactions among machines, humans and data entities. These interactions could be categorized as machine-to-machine (M2M), machine-to-human (M2H), and human-to-data (H2D), and involve many challenges such as collaborative trust, privacy, legibility and accountability. Dr Yadav is an active reviewer of many top-tier ACM/IEEE IoT and networking conferences and journals. Dr. Yadav leads ACM-W UK professional chapter and is featured as "People of ACM Europe" and among the top ten N2Women Rising Star in Computer networking and communications in 2020. Research themes: E-Sports Use of IoT in Games Gamifications Citizen Science ​ poonam.yadav@york.ac.uk Email Mastodon https://poonamyadav.net Other links Website https://www.linkedin.com/in/pyadav/ LinkedIn https://twitter.com/@pooyadav Twitter https://github.com/pooyadav Github ​ ​ Themes Design & Development Esports Game Data - Previous Next

< Back Making Space for Social Time: Supporting Conversational Transitions Before, During, and After Video Meetings Link ​ Author(s) C Gonzalez Diaz, J Tang, A Sarkar, S Rintel Abstract ​ More info TBA ​ Link

< Back Emergence in the Expressive Machine Link ​ Author(s) L Dekker Abstract ​ More info TBA ​ Link

< Back Hardness in Markov Decision Processes: Theory and Practice Link ​ Author(s) M Conserva, P Rauber Abstract ​ More info TBA ​ Link

< Back A Survey on AI and Ethics: Key factors in building AI trust and awareness across European citizens. Link ​ Author(s) Cristian Barrué, Atia Cortés, Alessandro Fabris, Francesca Foffano, Long Pham, Teresa Scantamburlo Abstract ​ More info TBA ​ Link

< Back ​ Luke Farrar University of York ​ iGGi PG Researcher ​ ​ Luke Farrar is an iGGi PhD student at The University of York undertaking research in Flexible and Realistic Character Animations in Complex and Dynamic Environments. Luke's research focuses through his bachelor's and master's degrees were on applying machine learning to interesting and unique settings. In his bachelor's he focused on creating an application for individuals that suffered from cognitive impairments through the use of the "Microsoft HoloLens" and machine learning to allow those individuals to maintain a semblance of everyday life. In his postgraduate Luke focused on using machine learning to generalise high-fidelity scientific simulations to rapidly generate predictions for parameter combinations that had not yet been sampled in order to accelerate the production of new results. Luke revels in all things AI, knowing that there is always more to learn and seeks to continually deepen his understanding around AI. A description of Luke's research: Modern games have an increasing focus on hyper-realism and immersion to better capture the attention of players. One of the ways that games can break this immersion is by having animations that break the flow of movement or actions through the use of predefined animations. Motion matching is a solution for predicting the best next frame of an animation by looking at the pose and user trajectory. The downside however, is that when you increase the amount of possible animations in the database the runtime cost also increases. A solution was proposed known as 'learned motion matching' (Holden et al., 2020) which takes the positive properties of motion matching but also achieves the scalability of neural-network-based generative models. This project will explore and improve the learned motion matching method through implementation of memory layers to improve accuracy without the sacrifice of increasing runtime costs. A restructuring and adaptation of the existing machine learning neural network used could also improve the learned motion matching method as breaking down each step of the learned motion matching at each step could uncover optimisations that are not initially visible. Another way restructuring could improve the learned motion matching is through creating a more succinct all-in-one approach which may streamline the process. ​ lukebfarrar@gmail.com Email Mastodon Other links Website https://www.linkedin.com/in/luke-farrar-3967b3243/ LinkedIn Twitter Github Supervisors: Dr Miles Hansard Dr Patrik Huber Dr James Walker ​ Themes Immersive Technology - Previous Next

< Back Data-Driven Audience Experiences in Esports Link ​ Author(s) A Kokkinakis, [...] P York, A Pedrassoli Chitayat, [...] B Kirman, J Hook, A Drachen, M Ursu, F Block 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. Player Research Limited

< Back ​ Dr Sarah West University of York ​ Supervisor ​ ​ Sarah West is an interdisciplinary researcher and practitioner working to bring diverse voices into research through participatory approaches, including citizen science. Sarah is currently Director of SEI York, a Centre of the Stockholm Environment Institute, a science-to-policy research institute, whose York Centre is at the University of York in the Department of Environment and Geography. She has used citizen science approaches to address topics as diverse as air pollution, biodiversity, parenting, and exploring community responses to Covid-19. Her projects mainly take place in the UK and Kenya. Sarah has spent over a decade designing, running and evaluating citizen science projects, and together with other SEI colleagues has written reports for Defra, UK Earth Observation Framework and journal articles exploring who participates in citizen science, their motivations for participation, and how volunteers can be recruited and retained. She is particularly interested in exploring how different messaging and communication affects participation in citizen science projects. ​ sarah.west@york.ac.uk Email Mastodon https://www.york.ac.uk/sei/staff/sarah-west/ Other links Website https://www.linkedin.com/in/sarah-west-59b82690/ LinkedIn https://twitter.com/SarahWest_SEI Twitter Github ​ ​ Themes Accessibility Design & Development Player Research - Previous Next