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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. Electronic Arts (EA)

< Back ​ Dr Luca Rossi Queen Mary University of London ​ Supervisor ​ ​ Luca Rossi is a Lecturer in Artificial Intelligence at Queen Mary University of London. His research expertise lies in the areas of structural pattern recognition, machine learning, data and network science. Within the context of IGGI, he is interested in applying graph machine learning techniques, particularly graph neural networks, to the modelling and analysis of games. He is also interested in supervising projects related to behavioural analytics and privacy issues in online gaming. ​ luca.rossi@qmul.ac.uk Email Mastodon https://blextar.github.io/luca-rossi/ Other links Website LinkedIn Twitter Github ​ ​ Themes Game AI Game Data - Previous Next

< Back ​ Amy Smith Queen Mary University of London ​ iGGi PG Researcher ​ Available for placement After completing a BA in Fine Art, at Bath School of Art and Design, Amy spent some years as a tattoo artist travelling and creating artworks. An interest in learning to code then led her to complete a conversion Masters degree in Computer Science at the University of Birmingham. Keen to preserve her interests in both a creative practice as well as a new interest in generative deep learning, Amy joined the IGGI program to explore these interests further under the guidance of Dr. Mike Cook, Dr. James Walker and Prof. Simon Colton. Amy's research is currently focused on the intersection between 'imaginative play', computational creativity and generative deep learning. This project explores whether the kind of novel text, image and video media produced by generative deep learning algorithms can be used to provoke and stimulate the imaginative, ideation and visualisation capabilities of the user as they interact with this cutting edge technology. To date, her work has been accepted to several conferences, including the International Conference on Computational Social Science, AAAI, the International Conference on Computational Creativity, and EvoMusArt. Amy hopes to further encourage and explore the fruits of a close collaboration between human creativity and creative AI. ​ amyelizabethsmith01@gmail.com Email Mastodon http://aialchemy.media.mit.edu Other links Website https://www.linkedin.com/in/amy-smith-791784173 LinkedIn https://twitter.com/AmysImaginarium Twitter Github Supervisors: Dr Mike Cook Prof. Simon Colton Dr James Walker Featured Publication(s): Generative Search Engines: Initial Experiments Themes Creative Computing Player Research - Previous Next

< Back Lateralization of impedance control in dynamic versus static bimanual tasks Link ​ Author(s) N Pena-Perez, J Eden, I Farkhatdinov, E Burdet, A Takagi Abstract ​ More info TBA ​ Link

< Back A comparison of the effects of haptic and visual feedback on presence in virtual reality Link ​ Author(s) JK Gibbs, M Gillies, X Pan Abstract ​ More info TBA ​ Link

< Back ​ Andrew Martin Queen Mary University of London ​ iGGi Alum ​ ​ Applications in game development for programming language theory and AI Modern game development is highly iterative. Iteration is usually discussed in terms of a team completing design iterations, but can also be considered at the level of an individual developer attempting to complete a task, or experimenting with some ideas. At this level, the feedback loop provided by the tool becomes critical. Programming environments in particular often have a very poor feedback loop. Programming feedback can be thought of in terms of how quickly and seamlessly the user is able to observe the results of their work. This process is usually plagued with manual tasks and long pauses. It is common that a user will need to recompile, relaunch their program, and then manually recreate whatever state is required to observe the behaviour that they are working on. Frameworks like Elm, React and Vuejs are establishing a new norm of automatic hot-reloading with state preservation. These systems represent a branch of programming language research that is strongly focused on developer experience. In order to improve upon this work for game development, we must overcome the unique challenges that game development entails. Although the systems mentioned are all quite recent, there is a rich vein of research to draw on, which can be traced through dataflow programming, Smalltalk, Erlang, functional-reactive programming, Lisp and more. Predictive completions are considered by many to be a natural next-step in the evolution of live programming environments. An AI programming assistant would propose program fragments as completions or alternatives. The agent may seek to anticipate the user’s intent, or to provide creative suggestions. There is much relevant research in the fields of program synthesis, inductive logic programming, machine learning and genetic programming. One significant problem is how to smoothly and safely integrate a system like this into the user’s workflow. Many of the properties useful for safely enabling live programming features, such as isolation of side-effects, will also permit an AI agent to safely generate and execute code. Andy graduated from Imperial College London with an MEng in Computing in 2011. Following this he worked on game engine tools and technology at a startup called Fen Research, and then as a senior developer at a software consulting firm called LShift. In 2016 he spent six months working as a Research Associate in the Computational Creativity group at Goldsmiths, before starting his PhD. Please note: Updating of profile text in progress ​ Email Mastodon Other links Website LinkedIn Twitter Github ​ ​ Themes Game AI - Previous Next

iGGi PhD Projects 2023 Creative Computing This page displays the supervisor-proposed PhD projects on offer under the above stated theme: If you are interested in any of the projects listed and would like further details and/or to discuss, please email the project supervisor. Please note that you can also frame your own project independently granted that you have secured a supervisor's support. For a list of available supervisors please see the accepting students section of our website. ​ While iGGi has checked that the project descriptions listed below are within iGGi's scope , we wish to highlight that you are still responsible for ensuring that your proposal, too, is in line with this scope, and we would further like to point out that supervisor-framed projects are not prioritised in the application selection process: they are judged by the same criteria as applicant-framed proposals. For guidance to make sure that the proposal you submit (regardless of whether it has been supervisor-framed or created entirely by you) sits within iGGi's scope please refer to this link: https://iggi.org.uk/iggi-scope Navigate to other Themes on offer: Game AI Design & Development Player Research Game Audio Game Data Immersive Technology Creative Computing E-Sports Applied Games Back to ALL Projects Creative Computing ​ Novelty Optimisation Can we identify and automatically balance the right amount of novel content we serve to players? Price Creative Computing Duration Jeremy Gow, Sebastian Deterding Read More Creative Computing ​ Novel video narrative from recorded content This project constructs novel video narratives from recorded content under employment of deep learning tools and techniques. Price Creative Computing Duration Nick Pears Read More Load More

< Back ​ Prof. Simon Lucas Queen Mary University of London iGGi Co-Investigator Supervisor ​ ​ Simon Lucas is a professor of Artificial Intelligence and Head of the School of Electronic Engineering and Computer Science at Queen Mary University of London where he also heads the Game AI Research Group. He holds a PhD degree (1991) in Electronics and Computer Science from the University of Southampton. He is the founding Editor-in-Chief of the IEEE Transactions on Games and co-founded the IEEE Conference on Games. His research involves developing and applying computational intelligence techniques to build better game AI, use AI to design better games, provide deep insights into the nature of intelligence and work towards Artificial General Intelligence. He is the QMUL lead for the EPSRC-funded CDT in Intelligent Games and Game Intelligence (IGGI). He has supervised more that 15 PhD students to completion, most of them in Game AI. Research themes: Game AI Agents (RL, Monte Carlo Tree Search, Rolling Horizon Evolution) Learning Forward Models Automated Game Design, Procedural Content Generation Game AI for real-world problem solving ​ simon.lucas@qmul.ac.uk Email Mastodon https://www.eecs.qmul.ac.uk/profiles/lucassimon.html Other links Website LinkedIn https://twitter.com/simonmarklucas Twitter https://github.com/simon-lucas Github ​ ​ Themes Game AI - Previous Next

< Back ​ Connor Watts Queen Mary University of London ​ iGGi PG Researcher ​ Available for placement I am a machine learning research engineer and software developer with commercial experience deploying and maintaining models for start-ups and larger organizations. I have experience researching and developing novel algorithms, as well as designing custom environments for application in domains such as combinatorial optimization, finance and games. ​ c.watts@qmul.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/connor-watts-363354232/ LinkedIn Twitter https://ConnorWatts.github.io Github Supervisor: Dr Paulo Rauber ​ Themes Game AI - Previous Next

< Back ​ Michelangelo Conserva Queen Mary University of London ​ iGGi PG Researcher ​ Available for placement Michelangelo Conserva is a second year PhD researcher studying principled exploration strategies in reinforcement learning. He is particularly interested in randomized exploration and, more generally, Bayesian methods for reinforcement learning. He holds a BSc in Statistics, Economics and Finance from Sapienza, University of Rome and an MSc in Computational Statistics and Machine learning from University College of London. A description of Michelangelo's research: As a PhD student at Queen Mary University of London, Michelangelo aims to leverage Bayesian models to develop principled algorithms for reinforcement learning in the context of function approximations. The main challenge lies in finding a balance between computational costs and optimality. Evaluating such balance requires careful evaluation, which is currently lacking in reinforcement learning. ​ m.conserva@qmul.ac.uk Email Mastodon https://michelangeloconserva.github.io/ Other links Website https://www.linkedin.com/in/michelangeloconserva/ LinkedIn https://twitter.com/Michelangelo755 Twitter https://github.com/MichelangeloConserva Github Supervisors: Prof. Simon Lucas Dr Paulo Rauber Featured Publication(s): What are you looking at? Team fight prediction through player camera Posterior Sampling for Deep Reinforcement Learning Hardness in Markov Decision Processes: Theory and Practice Recurrent Neural-Linear Posterior Sampling for Nonstationary Contextual Bandits The Graph Cut Kernel for Ranked Data Themes Game AI - Previous Next

< Back ​ Rob Homewood Goldsmiths ​ iGGi Alum ​ ​ Personalised Aesthetics for Games The worldwide games industry is a huge market and as the spectrum of people who spend time playing games increases, there is more and more competition to create games that capture the attentions of a wide audience. Whilst games have been traditionally designed with specific cultural demographics in mind, a game that could dynamically match the cultural values of a range of demographics would maximize its potential market. Robert’s research looks at developing techniques for procedurally generating dynamic game assets that can be viewed as being relevant at a ‘per player’ level. He aims to do this by actively profiling a player’s social networks and building up a picture of the cultural references with which they identify. This knowledge could then be used to create game assets that match an aesthetic the player would likely feel comfortable with, allowing a more flexible decoupling between game mechanics and aesthetic during the design process. Designers could then focus on creating interesting game mechanics that could work in a variety of settings and the system would fill in the aesthetic detail based on the requirements of the individual player at run-time. Having studied in five countries, Robert is currently undertaking a PhD at Goldsmiths, University of London where he is part of the EPSRC funded IGGI (Intelligent Games and Games Intelligence) program. He also holds a Bachelor’s degree in Game Design and Production Management from the University of Abertay Dundee which included a year of studies at the George Mason University Computer Game Design Program. He also spent a year studying Serious Games at Masters level at the University of Skövde in Sweden (which has the longest running Serious Games program in the world). Robert has an active interest in the media arts field and has exhibited his work in three countries. Please note: Updating of profile text in progress ​ Email Mastodon Other links Website https://www.linkedin.com/in/robert-j-homewood-36906132/ LinkedIn https://twitter.com/@rob_homewood Twitter Github ​ ​ Themes Player Research - Previous Next

< Back Cooperative games with partial observability Link ​ Author(s) PR Williams, D Perez-Liebana, SM Lucas Abstract ​ More info TBA ​ Link