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< Back Dr Adrián Barahona-Ríos University of York iGGi Alum From 2018 and in collaboration with Sony Interactive Entertainment Europe, Adrián is researching strategies to increase the efficiency in the creation of procedural audio models for video games by using DSP and machine learning approaches. His main research interests, applied to the synthesis of sound effects, are generative deep learning (GANs, RNNs and VAEs) to synthesise raw audio and machine learning to find out the best parameters for a synthesiser to generate a target sound. Adrián has been enthusiastic about sound and more specifically about game audio since he began his studies. By the time he completed an HND in Creative Media Production in Madrid, he started working in the industry as a recording engineer in an ADR studio for the Spanish localisation of video games (such as Fallout 4, Until Dawn or Just Cause 3). He moved from Spain to the UK in 2015 to take a BA (top-up) in Music Production at the Southampton Solent University and an MSc in Sound Design at the University of Edinburgh immediately after. During that journey, he focused his career in procedural audio and explored ways to create models for interactive applications by using different techniques. adrian.barahona.rios@gmail.com Email Mastodon https://www.adrianbarahonarios.com/ Other links Website https://www.linkedin.com/in/adrianbarahona LinkedIn BlueSky https://github.com/adrianbarahona Github Supervisor Dr Tom Collins Featured Publication(s): Deep Learning for the Synthesis of Sound Effects NoiseBandNet: controllable time-varying neural synthesis of sound effects using filterbanks Sonifying energy consumption using SpecSinGAN SpecSinGAN: Sound Effect Variation Synthesis Using Single-Image GANs Synthesising Knocking Sound Effects Using Conditional WaveGAN Perception of emotions in knocking sounds: An evaluation study Perceptual Evaluation of Modal Synthesis for Impact-Based Sounds Illuminating Game Space Using MAP-Elites for Assisting Video Game Design Themes Creative Computing Game Audio - Previous Next

< Back George Long Queen Mary University of London iGGi PG Researcher Available for placement George is an IGGI PhD student interested in AI assisted game design, particularly in how it can be used to assist in the creation and balancing of game mechanics. After graduating with a BSc in Computer Science at the University of Essex, he joined IGGI in 2021 to be able to research how Artificial Intelligence can be applied specifically to reduce the prevalence of Min-Maxing in Role-Playing Games. A description of George's research: My research focuses on the concepts of Min-Maxing and Meta in Role-Playing Games, and how we can use AI assisted game design to reduce their prevalence. Min-Maxing in Role-Playing Game refers to the idea of building a character in a Role-Playing Game by maximising their positive traits while minimising negative ones, often through exploiting game mechanics. This can cause optimal strategies to emerge which not only have the potential to upset the game balance, but when these strategies become prominent enough in the community to form a Meta, it can have wider consequences such as the shunning of players deemed not to be using optimal strategies, and loss of creative choice when building characters. There are two methods I am looking into to reduce the effectiveness of Min-Maxing. The first is using AI to discover these Min-Maxed strategies. Secondly, how AI can be used in the game balancing process to identify and modify the mechanics which enable these strategies. Currently, I am focusing on the first method, with my research looking into how we can measure the effectiveness of units in combat scenarios to identify which units could be considered unbalanced. g.e.m.long@qmul.ac.uk Email Mastodon http://www.longhouse.dev Other links Website https://www.linkedin.com/in/georgelonghouse/ LinkedIn BlueSky Github Supervisor(s): Dr Diego Pérez-Liébana Featured Publication(s): PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games Themes Design & Development Game AI Game Data - Previous Next

< Back Dr Matthew Whitby University of York iGGi Alum Matthew Whitby is a games designer, and player experience academic investigating how games can shape how perspectives on a small or grand scale. In particular, his work considers how we can make the development of perspective challenging processes easier for game developers. Previously, Matthew has published his undergraduate dissertation within the Games Journal, which explored the creation and design of Games Installations. Games that make full use of their surrounding space, and in fact incorporate the real world with its digital counterpart. In addition, he’s worked with Motek Medical, a rehabilitation company based in Amsterdam, where he developed socially focused multiplayer applications. More recently, he attended CHI Play 2019 to present the foundational study of his PhD titled: “One of the Baddies All Along: Perspective Challenging Moments in Games”. He continues to develop this idea forward, while developing games (both digital and table-top) in his spare time. Matthew’s work hopes to answer; how games can challenge a player’s perspective, and if this is a phenomenon that can be intentionally designed for? matt_whitby@hotmail.com Email Mastodon https://www.matt-whitby.com Other links Website https://www.linkedin.com/in/matthew-whitby-b324ab83 LinkedIn BlueSky Github Supervisor(s): Prof. Sebastian Deterding Dr Jo Iacovides Themes Design & Development Player Research - Previous Next

< Back Ruizhe "Jay" Yu Xia Queen Mary University of London iGGi PG Researcher Available for placement Ruizhe has bachelor degrees in Mathematics and Physics and a master's degree in Artificial Intelligence. After a short time as a consultant he decided to pursue research into what got him into AI in the first place: game agents. He enjoys games of all kinds, but strategy and RPG games occupy a sizeable portion of his collection. AI agents that perform with superhuman skill in increasingly complex games have appeared in recent years, but these agents are not always useful to game developers. Players within a game exhibit significant variance in their skill levels and play styles. Therefore, game agents with similar variance would better represent the player base. The research Ruizhe proposes will focus on three areas: measuring skill and play styles, developing game agents that mimic a range of human play styles and skill levels, and making these agents human-like. Upon successful completion, this research will improve the game development process via automated playtesting and will enable the development of AI agents that are more engaging and interactive. r.yuxia@qmul.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/ruizheyuxia/ LinkedIn BlueSky Github Supervisor: Prof. Simon Lucas Dr Jeremy Gow Themes Game AI Game Data - Previous Next

< Back Dr Sebastian Berns Queen Mary University of London iGGi Alum Sebastian is a designer and researcher working on use-inspired fundamental research in generative machine learning for creative and artistic applications. Sebastian holds a master’s degree in artificial intelligence and has a background in visual communications. He has worked several years as an independent graphic and type designer with a specialisation in web development. His design work has been awarded national and international design prizes. A description of Sebastian's research: "Generative machine learning methods are trained on raw data, modelling the primary patterns that constitute typical examples. They enable the production of high-quality artefacts in very complex domains and provide useful models for generative systems, in particular in the visual arts and video games. However, modelling a training data distribution perfectly is less valuable for applications in art production and video games. In particular, our analysis of the use of generative models in visual art practices motivates the need to increase the output diversity of generative models. In my dissertation, I focus on diversity in generative machine learning for visual arts and video games. Our findings benefit the application of generative models in generative systems, quality diversity search, art production and video games. Rather than a ‘ground truth’ that needs to be modelled perfectly, we argue that training datasets are merely a limited snapshot of a complex world with inherent biases. To be useful for applications in visual arts and video games, generative models require higher output diversity. Relatedly, higher generative diversity benefits efforts of equity, diversity and inclusion by reducing harmful biases in generative models." s.berns@qmul.ac.uk Email Mastodon http://www.sebastianberns.com/ Other links Website LinkedIn BlueSky https://github.com/sebastianberns Github Featured Publication(s): Not All the Same: Understanding and Informing Similarity Estimation in Tile-Based Video Games Towards Mode Balancing of Generative Models via Diversity Weights Increasing the Diversity of Deep Generative Models Active Divergence with Generative Deep Learning--A Survey and Taxonomy Automating Generative Deep Learning for Artistic Purposes: Challenges and Opportunities Expressivity of Parameterized and Data-driven Representations in Quality Diversity Search First experiments in the automatic generation of pseudo-profound pseudo-bullshit image titles Generative Search Engines: Initial Experiments Adapting and Enhancing Evolutionary Art for Casual Creation. Creativity Theatre for Demonstrable Computational Creativity Bridging Generative Deep Learning and Computational Creativity NEST 2.18. 0 Active Divergence with Generative Deep Learning--A Survey and Taxonomy Themes Creative Computing - Previous Next

< Back Cameron Johnston Queen Mary University of London iGGi PG Researcher Available for placement Cam holds an MPhys in Theoretical Physics from the University of Edinburgh, wherein he combined infectious disease models with fluid dynamics to examine the validity of wastewater-based epidemiology as a method of passively monitoring the prevalence of COVID-19 in an urban population. During his studies, Cam began to learn game development as a way of improving his programming skills, using C++ and the Unreal Engine to develop a number of small projects and compete in game jams. After completing his MPhys, Cam was eager to take his experience developing physics simulations even further, which lead him to joining the iGGi programme. About Cam's Research: 'Impossible physical models' refer to digital models of any physics that differs from what would be experienced in everyday life. This covers 'incorrect' physics (disagreeing with observation but mathematically valid), and unfamiliar physics (that which is physically correct but irrelevant on a human scale). By creating interactive, virtual environments around these models, it becomes possible to experience the impossible. This research aims to explore the potential of 'impossible physical models' in the context of video games from the perspectives of game design and education. The project explores what work has already been done into this topic, expands on this work, and finds new areas to explore. The goal of this research is to introduce new relationships between physics and video games, and to engender developers to explore physics as a tool for design. cameron.johnston@qmul.ac.uk Email Mastodon http://crjohnston.com Other links Website https://www.linkedin.com/in/c-r-johnston/ LinkedIn BlueSky Github Supervisor: Dr Josh Reiss Themes Creative Computing Design & Development - Previous Next

< Back Dr Nathan John Queen Mary University of London iGGi Alum After graduating with a MEng in Computer Science from the University of Bristol, Nathan joined the games industry as a programmer, working for Climax Studios, Gaming Corps and Freejam, before moving to a career as a general software engineer, while still developing indie games on the side. His experiences across a range of industries sparked a passion for testing, and left him wondering if there were was to improve the automated testing in game development. Borne from an experiment Nathan had performed training AIs to play his indie game WarpBall, in which he found the agents solved for exploits in the authored AI rather than playing the game well, his research project proposes a novel method for improving the quality of behaviour of human authored agents by pitting them against trained agents and observing what bad behaviours/exploits the trained agents reveal. Authored agents refer to AI agents whose actions are explicitly designed by programmers using traditional techniques such as Utility functions, Behaviour Trees and state machines; trained agents refer to agents whose behaviour is learned by playing many games against the authored agents. n.m.john-mcdougall@qmul.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/vethan4/ LinkedIn BlueSky Github Supervisors: Dr Jeremy Gow Dr Laurissa Tokarchuk Themes Design & Development Game AI - Previous Next

< Back Prof. Peter Cowling Queen Mary University of London iGGi Director Supervisor Peter Cowling has led teams that have won £45 million for research into games and digital creativity. After decades of experience in novel models and algorithms for AI decision-making, his research is now targeted on finding and promoting promising research directions in AI, games and digital creative technology, to benefit people and wider society. Playful ideas, curiosity and games have a central role! As Principal Investigator, he led the teams which won the grants for IGGI (2014 and 2019) and Digital Creativity Labs (2015). He is a member of the Programme Advisory Board which informs strategy in the Digital Economy area of UK research council funding. He has sat on several research council grant funding prioritisation panels, chairing two. He has presented ideas for the use of games as a tool to influence and understand the human condition at a number of venues, including TEDx and 10 Downing Street. He has published over 100 papers, winning 2 best paper awards at AIIDE. His research technology has over 5 million installs in commercial games – he was invited to talk at GDC about that. He would be interested to supervise students whose research uses games as a tool to gather opinion or promote understanding: to identify research directions and harness the future potential of games, creativity and AI to benefit people and society. He is particularly interested in how games and other curious, creative things can help us to understand a world of complex interacting agents, each living a world created by their own thought (!). Research themes: Research visions for games and AI Game design/development to influence, inform and understand people and society Game AI peter.cowling@qmul.ac.uk Email Mastodon https://www.petercowling.com/ Other links Website https://uk.linkedin.com/in/peter-cowling-3590962 LinkedIn BlueSky Github Themes Applied Games Design & Development Game AI - Previous Next

< Back Alex Fletcher Queen Mary University of London iGGi Alum Alex Fletcher is a freelance audio engineer and junior game developer working on understanding the perceived flow and player experiences in mobile rhythm games and how a dynamic difficulty adjustment system would improve these experiences. The function of EEG and other biosensors as an additional measurement of player experience is of particular interest as further research in its use as an adaptive system. Other areas of research interest include game-based learning and games with a purpose. Please note: Updating of profile text in progress Email Mastodon Other links Website https://www.linkedin.com/in/alex-fletcher-64ab72176 LinkedIn BlueSky Github Themes Applied Games Game Audio Player Research - Previous Next

< Back Dr Yizhao Jin Queen Mary University of London iGGi Alum Currently a student at Queen Mary University of London (QMUL), I have delved deep into the realms of artificial intelligence and game design. With a passion for understanding the complexities behind real-time strategy (RTS) games and their dynamic, unpredictable nature, I have committed myself to contribute novel insights to this domain. Research: My primary research area is Hierarchical Reinforcement Learning (HRL) for Real-Time Strategy (RTS) games. RTS games, known for their intricate mechanics and vast decision spaces, present a formidable challenge for traditional AI approaches. By employing HRL, I aim to develop agents that can not only understand the multi-layered tactics and strategies of these games but also learn to adapt to ever-changing game scenarios efficiently. The main objectives of my research are: Better Generalization: To create agents that can seamlessly transition between different RTS games or various maps within the same game without extensive retraining. This involves understanding common strategic threads across multiple game domains. Efficient Training: RTS games are inherently time-consuming due to their vast decision spaces and prolonged gameplay. My research seeks ways to optimize the training process, ensuring that AI agents can learn faster and with fewer computational resources. acw596@qmul.ac.uk Email Mastodon Other links Website LinkedIn BlueSky https://github.com/decatt Github Supervisors: Prof. Greg Slabaugh Prof. Simon Lucas Themes Game AI Previous Next

< Back Yu-Jhen Hsu Queen Mary University of London iGGi PG Researcher I have always been interested in automation specifically within strategy games, starting from civilization 5. I have a background in Artificial Intelligence with a Master of Science degree from Queen Mary, University of London, with a focus on Game AI, Computer Vision and Machine Learning/Deep Learning. My research interests involve Game AI improvement in real-time turned-based games with the help of data science techniques. A description of Yu-Jhen's research: This project has two goals. Firstly, to improve the performance of MCTS (Monte Carlo Search Tree) implementation. Secondly, the goal is focused on building an AI agent that is able to win the game but also provide feedback information/data about it’s decisions to the players and designers. In order to achieve the goal, the plan of the project is to use different data science skills to enable the game AI agent to understand the utility of different actions and decrease the time needed for making decisions. The data collected can also help the game AI agent explain it’s behaviors, hence provided useful information/data for its users and designers. y.hsu@qmul.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/yujhenhsu/ LinkedIn BlueSky Github Supervisors: Dr Diego Pérez-Liébana Dr Raluca Gaina Featured Publication(s): Why Choose You?-Exploring Attitudes Towards Starter Pokémon Tribes: a new turn-based strategy game for AI research MCTS Pruning in Turn-Based Strategy Games. Themes Game AI Game Data - Previous Next

< Back Callum Deery University of York iGGi Alum Callum is a researcher and game developer investigating how real-time player experience measurement can be used to drive adaptive games. Aiming to embed player experience questionnaires into games in a way that doesn’t break immersion and presence, his PhD is focussed on leveraging the wide range of existing player experience questionnaires to improve games ability to adapt to players. This will involve exploring the states of immersion and presence: What is necessary to maintain them? What experiences can players reflect on without breaking immersion? How do we embed a questionnaire into an in-development game without disrupting the player experience? callum.deery@gmail.com Email Mastodon https://cfdj.itch.io/ Other links Website LinkedIn BlueSky Github Supervisors: Dr James Walker Dr Anna Bramwell-Dicks Themes Accessibility Design & Development Player Research - Previous Next