< Back Experiments in motivating exploratory agents Link Author(s) B Khaleque, M Cook, J Gow Abstract More info TBA Link

< Back Understanding how we make accessible games: Perspectives from the games industry and players with disabilities Link Author(s) J Kulik Abstract More info TBA Link

< Back The Basic Needs in Games (BANG) Model of Video Games and Mental Health: Untangling the Positive and Negative Effects of Games with Better Science Link Author(s) N Ballou Abstract More info TBA Link

< Back DuoMem: Towards Capable On-Device Memory Agents via Dual-Space Distillation Link Author(s) P Hosseini, O Bohdal, A Alajrami, A Maracani, I Castro, M Purver, M Ozay, ... Abstract More info TBA Link

< Back Toward an Ontology of Wargame Design Link Author(s) L Ouriques, CE Barbosa, J Kritz, G Xexéo Abstract More info TBA Link

< Back Embodied, in-medium design of VR game motion controls using interactive supervised learning Link Author(s) C Gonzalez Diaz Abstract More info TBA Link

< Back 10 Welcome to Revachol: Disco Elysium as virtual dark tourism Link Author(s) FS Nicholls Abstract More info TBA Link

< Back Bootstrap Your Own Teacher: Online Policy Distillation for Multi-Game Reinforcement Learning Link Author(s) DJ Byrne, M Tot, P Duckworth, C Bonnet, A Laterre, TD Barrett Abstract More info TBA Link

< Back GAIG Meetup The recent Game AI Meetup took place on 01 March 2023. Talks and presentation included: Jakob Foerster (University of Oxford, UK): Opponent-Shaping and Interference in General-Sum Games Original talk abstract: In general-sum games, the interaction of self-interested learning agents commonly leads to collectively worst-case outcomes, such as defect-defect in the iterated prisoner's dilemma (IPD). To overcome this, some methods, such as Learning with Opponent-Learning Awareness (LOLA), shape their opponents' learning process. However, these methods are myopic since only a small number of steps can be anticipated, are asymmetric since they treat other agents as naive learners, and require the use of higher-order derivatives, which are calculated through white-box access to an opponent's differentiable learning algorithm. In this talk I will first introduce Model-Free Opponent Shaping (M-FOS), which overcomes all of these limitations. M-FOS learns in a meta-game in which each meta-step is an episode of the underlying (``inner'') game. The meta-state consists of the inner policies, and the meta-policy produces a new inner policy to be used in the next episode. M-FOS then uses generic model-free optimisation methods to learn meta-policies that accomplish long-horizon opponent shaping. I will finish off the talk with our recent results for adversarial (or cooperative) cheap-talk: How can agents interfere with (or support) the learning process of other agents without being able to act in the environment? Vanessa Volz ( modl.ai ): Establishing Trust in AI-based Tools for Game Development Original talk abstract: AI-based tools to support the game development process have long been a topic in Game AI research, with popular publications in testing, churn prediction, asset, level and even game generation. However, the adaptation of these techniques from the games industry has been hesitant at best: The small-scale and simplified examples researchers use to demonstrate their work understandably only seldom convince the industry to risk investing in AI tools. In this talk, I will speak about my experience establishing trust in AI-based tools to support creative processes in game development. Having worked on this topic in both industry and academia, I will address issues ranging from establishing a common language and explaining AI behaviour to issuing performance guarantees via benchmarking and theoretical analysis. Mike Preuss (Leiden University, The Netherlands): In the eye of the storm? Where are we going with game AI? Original talk abstract: Looking back at the last 10 years of research in Game AI we find that Big Tech research has shaken up things quite a lot. A number of challenges were resolved in record time (Go, StarCraft, etc) and AI algorithm development is probably still increasing in speed. However, it seems that the use of AI in game-making has not changed that much, and academic research often opts for "smaller problems", slowly turning towards Human-Centered AI as possibly most important general research direction. How can we approach the next leap predicted by Alex Champandard 10 years ago of really intelligent game AI? And where would we want that? Mike presents some inconclusive thoughts and ideas on future developments. The Game AI Meetup takes place several times a year. To sign up and receive updates, please register/join here: https://www.meetup.com/game-ai-meetup-gaim-of-london/ Previous 1 Mar 2023 Next

< Back The Effects of a Soundtrack on Board Game Player Experience Link Author(s) T Farkas, A Denisova, S Wiseman, R Fiebrink 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. University of Geneva

< Back A study of professional awareness using immersive virtual reality: the responses of general practitioners to child safeguarding concerns Link Author(s) X Pan, T Collingwoode-Williams, A Antley, H Brenton, B Congdon, ... Abstract More info TBA Link