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< Back Investigating sensorimotor contingencies in the enactive interface Link Author(s) JK Gibbs, K Devlin Abstract More info TBA Link

< Back Online-Trained Fitness Approximators for Real-World Game Balancing Link Author(s) M Morosan, R Poli Abstract More info TBA Link

< Back The Relationship of Future State Maximization and von Foerster's Ethical Imperative Through the Lens of Empowerment Link Author(s) C Guckelsberger, C Salge, D Polani Abstract More info TBA Link

< Back Beyond the Meta: Leveraging Game Design Parameters for Patch-Agnostic Esport Analitics Link Author(s) A Pedrassoli Chitayat, F Block, J Walker, A Drachen Abstract More info TBA Link

< Back G487 (P) Is clinician gaze and body language associated with their ability to identify safeguarding cues? Link Author(s) A Powell, T Collingwoode-Williams, N Schindler, SX Pan, C Fertleman Abstract More info TBA Link

< Back Metagame Autobalancing for Competitive Multiplayer Games Link Author(s) D Hernandez, CTT Gbadamosi, J Goodman, JA Walker Abstract More info TBA Link

iGGi PhD Projects - listing iGGi PhD Projects 2022 Industry-led Projects This page displays industry-proposed PhD projects to start September 2022. Note: The deadline for applications is Monday 13 June, 12:00 noon BST. We strongly suggest that potential applicants contact the supervisor(s) of their chosen project to develop a proposal as soon as possible (see below). The positions are fully funded and full-time for four years starting September 2022 (PhD fees plus a tax-free stipend to cover living costs). The PhD researchers will be based at Queen Mary University of London (QMUL) . The EPSRC Centre for Doctoral Training in Intelligent Games and Game Intelligence (iGGi) is the world's largest PhD programme aimed at games research, with over 70 current iGGi PhD researchers. iGGi covers a wide range of scientific and creative disciplines, encouraging team work and team play. iGGi researchers work closely with the games industry. To apply please follow the instructions on our Apply page https://iggi.org.uk/apply These two projects have been proposed by our partner Creative Assembly , one of the UK’s largest games studios which has been established for 34 years and is the author of the Total War games series. If you are interested in one of the projects and would like further details and/or to discuss, please contact the respective project supervisor(s) via the email address given in the project description. Your proposal should be developed via (email) discussion with the prospective supervisor(s). Please also make sure that the proposal you submit sits within iGGi's scope .

< Back Forging new narratives Link Author(s) NB Ben-Meir, L Dudek, Tomica, A Wilson Abstract More info TBA Link

< Back Rokas Volkovas Queen Mary University of London iGGi Alum Application of Neuroevolution to General Video Game Playing In the field of artificial intelligence, great advancements in developing AI capable of playing specific games has been made over last few decades. Over the years, the potential of General Game Playing (GGP) AI, was realized, and thus a new area of research was spawned, focusing mainly on turn-based board games. Rapidly expanding, it was just recently extended to include video games and has morphed into General Video Game Playing (GVGP). The studies in this space of AI are highly attractive due to their solution capacity of being highly transferable. As the field is relatively new, there are many different paths to explore. Some effort has already been put into incorporating the established Genetic Algorithm techniques into the area. The goal of the proposed research is to further develop models using the more complex evolutionary algorithms to find generalist solutions to the problems exposed in GVGP. More specifically, the research will aim to discover the appropriate applications and the modifications necessary of approaches such as Competitive Coevolution, circumventing its drawbacks and evolving populations capable of playing multiple games. Furthermore, in addition to other methods it will be concerned with the application of models developing generalist memory on a slower scale evolution (compared to individual in a population) with continuous state perturbations, to find closer to optimum results - adapting networks of individuals to the fitness landscape. In order to reach the goals of the research a number of experiments will be conducted, using a select few video games as a base performance measure. Training the populations evolved will involve tuning the evolutionary operators as well as altering pre-designed system be- haviours to suitably compare the viability of applied procedures. The success of bridging EA with GVPG, along with its advantages and drawbacks in the field will be readily deter- mined, comparing the solutions found to those of other existing approaches. Specifically, the similarity of the behaviour in evolvability using genetic networks searching for solutions and learning theory, via neural networks, has recently been suggested. Evolution is defined to not have any foresight, but models were built showing how it can remember previously discovered solutions, which would imply that natural selection leans towards long term evolvability. Kostas Kouvaris et. al. further establishes the underlying equivalence of the approaches, applying machine learning techniques to improve the generalisation of EA. The generalization allows combining the features from previous experience to find individuals with new feature combinations, better adapted to unseen environments. Were the exploratory learning methods developed in EA to perform no less satisfactorily in the gaming industry environment, given enough sample data from a handful of well defined behaviours, the AI units could be trained to adapt to the new levels they are placed in. In theory, this would then translate to the same amount of effort producing a larger variety of content or, alternatively, producing the same amount of content with less effort, distributing the excess to other areas of development or eliminating it to lower the total production cost. Rokas is an MEng Electronic Engineering graduate from University of Southampton. Initially, pushed away from programming in school due to being taught Pascal, he realized its power in the compulsory C course in University. Applying the knowledge to building games caused the gradual shift from electronics to software development, with the 4th year modules all having the CS tag. During the undergraduate studies Rokas held the UKESF scholarship and did 2 summer internships at Imagination Technologies. Interests in game and software development got him researching neuroevolutionary machine learning for video games. Please note: Updating of profile text in progress Email Mastodon Other links Website LinkedIn BlueSky Github Featured Publication(s): Automatic Game Tuning for Strategic Diversity Practical Game Design Tool: State Explorer Extracting learning curves from puzzle games Mek: Mechanics prototyping tool for 2d tile-based turn-based deterministic games Diversity maintenance using a population of repelling random-mutation hill climbers Themes Game AI - Previous Next

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. BlitzGame Studios

< Back Testing game mechanics in games with a purpose for NLP applications Link Author(s) C Madge, J Chamberlain, R Bartle, U Kruschwitz, M Poesio Abstract More info TBA Link

< Back Automated game balancing in Ms PacMan and StarCraft using evolutionary algorithms Link Author(s) M Morosan, R Poli Abstract More info TBA Link