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. The Chinese Room

< Back Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning Link Author(s) R Sasso, M Sabatelli, MA Wiering Abstract More info TBA Link

< Back Brain Drain Optimization (BRADO) Algorithm to Solve Multi-Objective Expert Team Formation Problem in Social Networks Link Author(s) A Basiri, E Masehian, F Taghiyareh, P Hosseini Abstract More info TBA Link

< 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 MAP-Elites to Generate a Team of Agents that Elicits Diverse Automated Gameplay Link Author(s) C Guerrero-Romero, D Perez-Liebana 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 Robust Imitation Learning for Automated Game Testing Link Author(s) PV Amadori, T Bradley, R Spick, G Moss 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 General win prediction from agent experience Link Author(s) RD Gaina, SM Lucas, D Perez-Liebana Abstract More info TBA Link

< Back Metrics of games-with-a-purpose for NLP applications Link Author(s) J Chamberlain, R Bartle, U Kruschwitz, C Madge, M Poesio 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. Meaning Machine

< Back Mapping Farmed Landscapes from Remote Sensing Link Author(s) M Conserva, A Wilson, C Stanton, V Batchu, V Gulshan Abstract More info TBA Link