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  • Daniel Gomme

    < Back ​ Dr Daniel Gomme University of Essex ​ iGGi Alum ​ ​ Players have underlying expectations of the opponents they play against in strategy games: don't break the rules, provide a sense of tension, be able to communicate plans... AI doesn't always fulfil these. Dan's focus is on finding ways to better fulfil those expectations - and even to overtly change them - in order to improve player experience. With qualitative tools and in-game testing, he's found several concrete design mechanisms that can further that goal. ​ daniel.gomme@yahoo.co.uk Email Mastodon Other links Website https://www.linkedin.com/in/daniel-gomme/ LinkedIn https://www.twitter.com/dan_gomme Twitter https://github.com/OctarineSourcerer Github Supervisor Prof. Richard Bartle Featured Publication(s): Player Expectations of Strategy Game AI Playing with Dezgo: Adapting Human-AI Interaction to the Context of Play Strategy Games: The Components of A Worthy Opponent Distributed Social Multi-Agent Negotiation Framework For Incomplete Information Games Tools To Adjust Tension And Suspense In Strategy Games: An Investigation Themes Design & Development Game AI Player Research - Previous Next

  • Streaming Behaviour: Livestreaming as a Paradigm for Analysis of Emotional and Social Signals

    < Back Streaming Behaviour: Livestreaming as a Paradigm for Analysis of Emotional and Social Signals Link ​ Author(s) C Ringer, MA Nicolaou Abstract ​ More info TBA ​ Link

  • jozef-kulik

    < Back ​ Dr Jozef Kulik University of York ​ iGGi Alum ​ ​ Jozef’s first study has focused on developing a better understanding of the challenges and barriers to making accessible games. This identified a vast array of personal, organisational, and external factors which contribute to the difficulties that developers experience when seeking to make their games more accessible, and also identifies avenues which might be helpful. One key finding in this research was that one of the biggest challenges that developers experience relates to a lack of lived experience with disability, or knowledge of the player experience with disabilities. My most recent research is focused on how to effectively extract that knowledge from players with disabilities, then insert it into a large studio within the UK. This research takes a multi-pronged approach to assisting developers in making more accessible games. First by directly assisting a studio with knowledge about their games, second generating potentially transferable knowledge on accessibility issues and player experience for the rest of the industry, and exploring how research methods such as diary study methodology can be valuable in extracting data from natural play environments with people with disabilities. ​ joe.kulik@york.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/jozef-kulik-a62516140/ LinkedIn https://twitter.com/ChronoJoe Twitter Github Supervisors: Prof. Paul Cairns Dr Jen Beeston Featured Publication(s): A Qualitative Investigation of Real World Accessible Design Experiences within a Large Scale Commercial Game Development Studio Grounded theory of accessible game development What makes icons appealing? The role of processing fluency in predicting icon appeal in different task contexts Themes Accessibility Player Research - Previous Next

  • Dominik Jeurissen

    < Back ​ Dominik Jeurissen Queen Mary University of London ​ iGGi PG Researcher ​ ​ I have always been fascinated by automating complex tasks. As a result, my bachelor's focused on software development paired with applied mathematics, and my master's focused on Artificial Intelligence. I'm particularly interested in reinforcement learning (RL) and continual learning. With the recent hype around large language models (LLMs), I am now focusing on utilizing LLMs to play games. I spent much of my free time playing board games with friends, jogging, cooking, and learning new things. A description of Dominik's research: Playtesting Games with Large Language Models With tight deadlines and a constantly evolving game, properly testing a game is challenging. Using AI agents to simplify this work sounds promising, but machine learning is often too slow, and manually implementing the agents takes time. As such, one particularly exciting application for QA is to use Large Language Models (LLMs) as zero-shot game-playing agents. LLM-based agents can play games without pre-training, making them a valuable asset for testing a constantly changing game. But how well do they play games? What are their strengths, and what do they struggle with? My research focuses on answering these questions and more. ​ d.jeurissen@qmul.ac.uk Email https://commandercero.github.io/ Mastodon Other links Website https://www.linkedin.com/in/dominik-jeurissen/ LinkedIn Twitter https://github.com/CommanderCero Github Supervisors: Dr Diego Pérez-Liébana Dr Jeremy Gow Featured Publication(s): Playing NetHack with LLMs: Potential & Limitations as Zero-Shot Agents PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games Generating Diverse and Competitive Play-Styles for Strategy Games PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games Automatic Goal Discovery in Subgoal Monte Carlo Tree Search Game state and action abstracting monte carlo tree search for general strategy game-playing Portfolio search and optimization for general strategy game-playing The Design Of" Stratega": A General Strategy Games Framework Themes Design & Development Game AI Game Data - Previous Next

  • Festive Wishes from iGGi | iGGi PhD

    < Back Festive Wishes from iGGi Festive Wishes from iGGi As another calendar year is drawing to a close, iGGi wishes you a happy & relaxing Festive Break and a good start into 2023 !!! In other news: Selected recordings of the last iGGi Conference are OUT NOW and can be accessed via our iGGi YouTube channel: iGGi CON 2022 Talks here https://youtube.com/playlist?list=PLrhB7hwYCdScgdnGraofca1YhhL3BfGyd iGGi CON 2022 Keynotes here https://youtube.com/playlist?list=PLrhB7hwYCdScXSOX3vDlGwUzQLu1WciTd Subscribe to https://www.youtube.com/@iGGiPhD for further updates! We look forward to seeing you all in the New Year! ​ Previous 21 Dec 2022 Next

  • Tania Dales

    < Back ​ Tania Dales University of York ​ iGGi PG Researcher ​ Available for placement Tania is a MSc by research Interactive Media graduate, and is experienced in qualitative data collection and analysis for video games, industry and educational purposes. They are a video game designer and developer, predominantly experienced with horror experiences and games which are a little strange, bizzare and uncomfortable but not directly horror. About Tania's research: Horror has the unique ability to leave lasting impressions upon a person, often this isn’t utilized as much as it could be and a video game may lean more towards stereotypical genre tropes such as jump scares and scary stories. A lasting emotional response appears to be something that occurs when a player is no longer engaged in gameplay after the credits have rolled and they have moved on. Often, it appears to stem from a moment of physical discomfort, such as repulsion or disgust, or from psychological phenomena such as unease and distrust which we see discussed predominately concerning the uncanny valley theory. Lasting emotional responses, and how they happen as a result of horror video game design is what my research seeks to understand. In particular, can we identify moments where a player experiences a lasting and memorable emotional response that is a result of design? Are there moments in video games where a player experiences an emotional response that was unintended by a designer as a result of a specific design element? Can we utilize this knowledge and understanding of the lasting impact video games have on a player to design authentic and innovative horror game experiences? By understanding what may prompt lasting emotional responses, we could begin to build a toolkit that can be utilized by indie game developers. ​ tania.dales@york.ac.uk Email Mastodon Other links Website http://www.linkedin.com/in/tania-dales-268912197 LinkedIn Twitter Github Supervisor: Dr Ben Kirman ​ Themes Design & Development Game AI Immersive Technology Player Research - Previous Next

  • Cristiana Pacheco

    < Back ​ Dr Cristiana Pacheco Queen Mary University of London ​ iGGi Alum ​ ​ Cristiana is a researcher with a passion for game development. Her research explores how to assess believability in video games and model/develop human-like behaviour. In addition, her research investigates applying these techniques in general, rather than a single specific game. She finished her BSc in Computer Games in Essex, where she also worked as a research assistant for an autonomous car racing project. She then started her PhD at Queen Mary University of London focused on games believability. Since, she has completed her placement at Ninja Theory, where she collaborated with Microsoft Research in Project Paidia. This opportunity provided experience with both game development and research. As a PhD student in her last year, she is working on the modelling of players through gameplay data and how this can be used to develop more human-like AI. The goal is to combine her research concepts into agents that do not always play to win, but rather present a diverse set of behaviours. ​ c.pacheco@qmul.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/cpache111/ LinkedIn https://twitter.com/Pacheco_CrisP Twitter https://github.com/Cpache1 Github Supervisor(s): Prof. Richard Bartle Dr Laurissa Tokarchuk Dr Diego Pérez-Liébana Featured Publication(s): Believability Assessment and Modelling in Video Games Predictive models and monte carlo tree search: A pipeline for believable agents Discrete versus Ordinal Time-Continuous Believability Assessment Trace it like you believe it: Time-continuous believability prediction Studying believability assessment in racing games PAGAN for Character Believability Assessment Rolling Horizon Co-evolution in Two-player General Video Game Playing Themes Creative Computing - Previous Next

  • Dr Changjae Oh

    < Back ​ Dr Changjae Oh Queen Mary University of London ​ Supervisor ​ ​ Changjae Oh joined Queen Mary University of London (QMUL) in September 2019 as a Lecturer at the School of Electrical Engineering and Computer Science (EECS). He was a postdoctoral researcher at QMUL EECS from 2018 to 2019. He received a PhD in Electrical and Electronic Engineering in 2018 at Yonsei University, South Korea. His research expertise spans a range of researches that are based on visual signals, such as image processing, computer vision, and vision-based machine perception, combined with machine/deep learning. Within the topics with IGGI, he is particularly interested in students who want to investigate the topics about vision-based AI perception in a game environment and game engines for real-robot perception. ​ c.oh@qmul.ac.uk Email Mastodon https://eecs.qmul.ac.uk/~coh/ Other links Website https://www.linkedin.com/in/changjae-oh-42a36685 LinkedIn Twitter Github ​ ​ Themes Applied Games Game AI - Previous Next

  • Strategy Games: The Components of A Worthy Opponent

    < Back Strategy Games: The Components of A Worthy Opponent Link ​ Author(s) D Gomme, R Bartle Abstract ​ More info TBA ​ Link

  • Nice is Different than Good: Longitudinal Communicative Effects of Realistic and Cartoon Avatars in Real Mixed Reality Work Meetings

    < Back Nice is Different than Good: Longitudinal Communicative Effects of Realistic and Cartoon Avatars in Real Mixed Reality Work Meetings Link ​ Author(s) GC Dobre, M Wilczkowiak, M Gillies, X Pan, S Rintel Abstract ​ More info TBA ​ Link

  • Rinascimento: using event-value functions for playing Splendor

    < Back Rinascimento: using event-value functions for playing Splendor Link ​ Author(s) I Bravi, SM Lucas Abstract ​ More info TBA ​ Link

  • iGGi Studentships - Home Fees Candidates | iGGi PhD

    < Back iGGi Studentships - Home Fees Candidates iGGi SPECIAL RECRUITMENT ROUND - home-fees only - With focus on home candidates only, we are inviting applications for studentships for the iGGi PhD Programme. Each studentship includes four years of fully funded (fees and stipend at UKRI rate) full-time study starting September 2023. The PhD researchers will be based at Queen Mary University of London or University of York (depending on which uni the chosen primary supervisor belongs to). To apply please follow the instructions on our Apply page Submit your full application by Monday 15 May 2023, 12:00 noon BST. ​ Previous 5 Apr 2023 Next

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