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< Back Dr Tony Stockman Queen Mary University of London Supervisor Dr Stockman is an interaction designer/researcher who investigates how technology can enhance accessibility and improve human performance. He is particularly interested in technology to support spatial cognition and wayfinding, health monitoring and improve performance levels in sport and music. This includes the role of games in simulating these domains and supporting skill acquisition and enhanced performance. He is a Board member and former president of the International Community for Auditory Display ( www.icad.org ). He has organised 6 international workshops on a range of HCI topics, and has been on the organising committee of 10 international HCI-related conferences. Topics on which he has recently published include participatory design and prototyping, auditory overviews for route guidance, self monitoring of biological signals and accessible collaborative working. He is particularly interested in supervising students with a Computer Science, Electrical Engineering, HCI, or behavioural sciences background on the following topics: Simulation to support accessibility and skill acquisition in team sports Intelligent audio mostly games to support learning Intelligent Audio or audio-haptic approaches to health monitoring and biofeedback Intelligent systems to support individual or collaborative music making Research themes: Intelligent simulation systems Interaction design for simulated sports Game Audio and Music Game Design Games with a Purpose t.stockman@qmul.ac.uk Email Mastodon Other links Website LinkedIn BlueSky Github Themes Applied Games Design & Development Esports Game AI Game Audio Player Research - Previous Next

< Back Matt Bedder University of York iGGi Alum Abstraction-Based Monte Carlo Tree Search. (Industry placement at PROWLER.io) Monte Carlo Tree Search is a popular artificial intelligence technique amongst researchers due to the remarkable strength by which it can play many games. This technique was prominently used as the basis for AlphaGo, the AI by Google DeepMind that became the first of its kind to beat professional human players at the game Go. But despite lots of interest from academics into Monte Carlo Tree Search, the technique has seen little use in the games industry - due in part to how it is not fully understood, and due to how complex it is to implement into large games. Matthew’s research is looking into how game abstractions can be used to help implement and optimise Monte Carlo Tree Search into existing commercial video games. Semi-automated methods for domain abstraction are being investigated, with the aim of making it fast and easy for game developers to be able to implement Monte Carlo Tree Search into their products, and to exploit the wealth of academic research into this technique. Matthew is currently studying towards his PhD at the University of York, having previously graduated for the Department of Computer Science with a MEng in Computer Science with Artificial Intelligence. Before starting his PhD, Matthew spent a year at BAE Systems Advanced Technology Centre working on contracts with the European Space Agency, and has performed research into vertebrae models of Parkinson's disease with York Centre for Complex Systems Analysis. Please note: Updating of profile text in progress Email Mastodon Other links Website https://linkedin.com/pub/matthew-bedder/80/2a7/a51/ LinkedIn BlueSky Github Featured Publication(s): Characterization and classification of adherent cells in monolayer culture using automated tracking and evolutionary algorithms Computational approaches for understanding the diagnosis and treatment of Parkinson's disease Automated motion analysis of adherent cells in monolayer culture Themes Game AI - Previous Next

< Back Shopna Begum Queen Mary University of London iGGi Administrator iGGi Admin iGGi Administrator at QMUL Shopna is part of the iGGi Admin Team which is responsible for the smooth running of iGGi. In her role as iGGi QMUL Administator she provides administrative services and pastoral care to PhD students and assists the iGGi QMUL Manager in key aspects of the Centre's management. shopna.begum@qmul.ac.uk Email Mastodon Other links Website LinkedIn BlueSky Github Themes - Previous Next

< Back Playing with evolution Link Author(s) RD Gaina Abstract More info TBA Link

< 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

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. Sony Interactive Entertainment

< Back How To Save A World: The Go-Along Interview as Game Preservation Methodology in Wurm Online Link Author(s) F Smith Nicholls, M Cook Abstract More info TBA Link

< Back Dr Daniel Berio Goldsmiths iGGi Alum AutoGraff: A Procedural Model of Graffiti Form. (Industry placement at Media Molecule) The purpose of this study is to investigate techniques for the procedural and interactive generation of synthetic instances of graffiti art. Considering graffiti as a special case of the calligraphic tradition, I propose a "movement centric" alternative to traditional curve generation techniques, in which a curve is defined through a physiologically plausible simulation of a (human) movement underlying its production rather than by an explicit definition of its geometry. In my thesis, I consider both single traces left by a brush (in a series of strokes) and the extension to 2D shapes (representing deformed letters in a large variety of artistic styles). I demonstrate how this approach is useful in a number of settings including computer aided design (CAD), procedural content generation for virtual environments in games and movies, computer animation as well as for the smooth control of robotic drawing devices. Daniel Berio is a researcher and artist from Florence, Italy. Since a young age Daniel was actively involved in the international graffiti art scene. In parallel he developed a professional career initially as a graphic designer and later as a graphics programmer in video games, multimedia and audio-visual software. In 2013 he obtained a Master degree from the Royal Academy of Art in The Hague (Netherlands), where he developed drawing machines and installations materializing graffiti-inspired procedural forms. Today Daniel is continuing his research in the procedural generation of graffiti within the IGGI (Intelligent Games and Game Intelligence) PhD program at Goldsmiths, University of London. Please note: Updating of profile text in progress Email Mastodon Other links Website LinkedIn BlueSky Github Featured Publication(s): Optimality Principles in the Procedural Generation of Graffiti Style SURFACE: Xbox Controlled Hot-wire Foam Cutter The role of image characteristics and embodiment in the evaluation of graffiti Emergence in the Expressive Machine The CyberAnthill: A Computational Sculpture Sketch-Based Modeling of Parametric Shapes Artistic Sketching for Expressive Coding Calligraphic stylisation learning with a physiologically plausible model of movement and recurrent neural networks Sequence generation with a physiologically plausible model of handwriting and Recurrent Mixture Density Networks AutoGraff: Towards a computational understanding of graffiti writing and related art forms Kinematics reconstruction of static calligraphic traces from curvilinear shape features Interactive generation of calligraphic trajectories from Gaussian mixtures Sketching and Layering Graffiti Primitives. Kinematic Reconstruction of Calligraphic Traces from Shape Features Expressive curve editing with the sigma lognormal model Dynamic graffiti stylisation with stochastic optimal control Computer aided design of handwriting trajectories with the kinematic theory of rapid human movements Generating calligraphic trajectories with model predictive control Learning dynamic graffiti strokes with a compliant robot Computational models for the analysis and synthesis of graffiti tag strokes Towards human-robot gesture recognition using point-based medialness Transhuman Expression Human-Machine Interaction as a Neutral Base for a New Artistic and Creative Practice 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. Safe In Our World

< Back Using a Team of General AI Algorithms to Assist Game Design and Testing Link Author(s) C Guerrero-Romero, SM Lucas, D Perez-Liebana Abstract More info TBA Link

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

< Back Noise reduction and targeted exploration in imitation learning for abstract meaning representation parsing Link Author(s) J Goodman, A Vlachos, J Naradowsky Abstract More info TBA Link