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< Back A data-driven approach for examining the demand for relaxation games on Steam during the COVID-19 pandemic Link Author(s) M Croissant, M Frister Abstract More info TBA Link

< Back Design Methods for Accessing the Pluriverse Link Author(s) Hadas Zohar, Nirit Binyamini Ben-Meir, Carolina Ramirez-Figueroa, Danielle Barrios-O'Neill, Michal Pauzner, Oded Kutok, Laura Dudek, Erin Robinson Abstract More info TBA Link

< Back From social media to artificial intelligence: improving research on digital harms in youth Link Author(s) KL Mansfield, S Ghai, T Hakman, N Ballou, M Vuorre, AK Przybylski Abstract More info TBA Link

< Back iGGi Talk at Develop:Brighton - Dominik Jeurissen iGGi PG Researcher Dominik Jeurissen held a talk on " LLM Agents For QA - Potential & Limitations " at this year's Develop:Brighton conference. Abstract: 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 to test a constantly changing game. But how well do they play games? What are their strengths, and what do they struggle with? In this session, we will review how to implement zero-shot agents with LLMs and show examples of existing LLM-based game-playing agents. We will also show that although these agents have many limitations, they have the potential to be a valuable tool for QA to automate many repetitive tasks. The objectives of Dominik's talk were to provide the audience with an overview of the cutting-edge research on LLM-based zero-shot game-playing agents show what these agents can do well and what their limitations are give practical tips on how to utilize LLM agents as QA tools Dominik's talk has been recorded and will be made available to Develop ticket holders. Please contact Dominik directly if you have any queries regarding the presentation. Previous 11 Jul 2024 Next

< Back Rolling horizon evolutionary algorithms for general video game playing Link Author(s) RD Gaina, S Devlin, SM Lucas, D Perez-Liebana Abstract More info TBA Link

< Back Machine Learning with Applications Link Author(s) Charles Ringer, Sondess Missaoui, Victoria J Hodge, Alan Pedrassoli Chitayat, Athanasios Kokkinakis, Sagarika Patra, Simon Demediuk, Alvaro Caceres Munoz, Oluseji Olarewaju, Marian Ursu, Ben Kirman, Jonathan Hook, Florian Block, Anders Drachen, James Alfred Walker Abstract More info TBA Link

< Back Measuring game experience using visual distractors Link Author(s) J Cutting Abstract More info TBA Link

< Back “That Darned Sandstorm”: A Study of Procedural Generation through Archaeological Storytelling Link Author(s) F Smith Nicholls, M Cook Abstract More info TBA Link

iGGi PhD Projects - listing iGGi PhD Projects 2023 Game AI 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 Game AI Automatic Evaluation of Tabletop Games This project proposal aims to research and develop methods to accurately evaluate the impact of modern Tabletop Games components in different aspects of gameplay. Price Game AI Duration Diego Pérez-Liébana Read More Game AI Principled and Scalable Exploration Techniques for Reinforcement Learning In this project, you will develop principled and scalable exploration techniques based on reducing model uncertainty. Price Game AI Duration Paulo Rauber Read More Game AI Evolving Perception for Game Agents This project will investigate game agents in open world games which evolve their senses and world representation alongside learning what actions to take in each state. We will evolve game agents with highly alien behaviours which nevertheless have high fitness in the open world environment, while investigating important scientific questions about how senses and world representations evolved in humans. Price Game AI Duration Alex Wade, Peter Cowling Read More Load More

< Back Defining the esports bettor: evidence from an online panel survey of emerging adults Link Author(s) H Wardle, E Petrovskaya, D Zendle Abstract More info TBA Link

< Back Portfolio search and optimization for general strategy game-playing Link Author(s) A Dockhorn, J Hurtado-Grueso, D Jeurissen, L Xu, D Perez-Liebana Abstract More info TBA Link

< Back Enabled players: The value of accessible digital games Link Author(s) P Cairns, C Power, M Barlet, G Haynes, C Kaufman, J Beeston Abstract More info TBA Link