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< Back European Strategy on AI: Are we truly fostering social good? Link Author(s) F Foffano, T Scantamburlo, A Cortés, C Bissolo Abstract More info TBA Link

< Back The relationship between psycho-environmental characteristics and wellbeing in non-spending players of certain mobile games Link Author(s) E Petrovskaya, D Zendle Abstract More info TBA Link

< Back Realistic and textured terrain generation using GANs Link Author(s) Ryan Spick, James Walker Abstract More info TBA Link

Evolving Perception for Game Agents Theme Game AI Project proposed & supervised by Alex Wade, Peter Cowling To discuss whether this project could become your PhD proposal please email: alex.wade@york.ac.uk < Back Evolving Perception for Game Agents Project proposal abstract: How does perception emerge? Hugely successful approaches to creating AI game playing agents such as MuZero, AlphaGo and AlphaStar learn the action to take in each state alongside a representation of the world to aid learning. For MuZero, AlphaGo and AlphaStar the representation is a prior distribution on how promising each move is in a given board position. The prior distribution can be seen as a highly effective way to perceive and simplify the game world, for greater decision-making fitness. In this project we will create game agents, for open world games such as Minecraft, which start from rudimentary sensors and simultaneously evolve a world representation while learning to make decisions leading to high fitness in the game world. We will investigate important scientific questions about how perception has evolved in humans, alongside creating interesting agents which might exhibit very weird and "alien" behaviours. Our internal representation of the world is conditioned both by evolution (for example, the physiology of the eye and brain) and also by learned experience. What sorts of perceptual systems might artificial agents develop in a simulated world? In this project we will develop simple 'open world' games into which we will release software agents with rudimentary sensory systems, possibly alongside human-controlled agents. These agents will be able to sense their world but not, initially, to perceive it (since perception is a combination of sensing and interpretation ). Both the sensory apparatus and the structure of the machine learning networks will be free to evolve (through genetic algorithms and reinforcement learning). Each generation will need to undergo a period of 'development' to train its networks on the current environment. We seek a motivated and talented student with a creative approach to research and skills in some of AI/machine learning, programming/game design, psychology/neuroscience and data analysis, and a willingness to learn new skills as necessary. Some travel to other international labs with an interest in this space may be possible. Supervisor: Alex Wade , Peter Cowling Based at:

< Back More than a bit of coding:(un-) Grounded (non-) Theory in HCI Link Author(s) T Cole, M Gillies Abstract More info TBA Link

< Back Kinematics reconstruction of static calligraphic traces from curvilinear shape features Link Author(s) D Berio, FF Leymarie, R Plamondon Abstract More info TBA Link

< Back WARDS: Modelling the Worth of Vision in MOBA's Link Author(s) AP Chitayat, A Kokkinakis, S Patra, S Demediuk, J Robertson, ... Abstract More info TBA Link

< Back Using Machine Learning to Design Movement Interaction in Virtual Reality Link Author(s) R Gibson, N Plant, C Gonzalez Diaz, B Martelli, M Zbyszyński, R Fiebrink, ... Abstract More info TBA Link

< Back TAG: Terraforming Mars Link Author(s) RD Gaina, J Goodman, D Perez-Liebana Abstract More info TBA Link

< Back Realtime control of sequence generation with character based Long Short Term Memory Recurrent Neural Networks Link Author(s) M Akten Abstract More info TBA Link

< Back Deep Learning for the Synthesis of Sound Effects Link Author(s) A Barahona-Rios Abstract More info TBA Link

< Back Rolling horizon evolution enhancements in general video game playing Link Author(s) RD Gaina, SM Lucas, D Perez-Liebana Abstract More info TBA Link