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< Back Dr Cristiana Pacheco Queen Mary University of London iGGi Alum Cristiana is a researcher with a passion for game development. Her research explores how to assess believability in video games and model/develop human-like behaviour. In addition, her research investigates applying these techniques in general, rather than a single specific game. She finished her BSc in Computer Games in Essex, where she also worked as a research assistant for an autonomous car racing project. She then started her PhD at Queen Mary University of London focused on games believability. Since, she has completed her placement at Ninja Theory, where she collaborated with Microsoft Research in Project Paidia. This opportunity provided experience with both game development and research. As a PhD student in her last year, she is working on the modelling of players through gameplay data and how this can be used to develop more human-like AI. The goal is to combine her research concepts into agents that do not always play to win, but rather present a diverse set of behaviours. c.pacheco@qmul.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/cpache111/ LinkedIn BlueSky https://github.com/Cpache1 Github Supervisor(s): Prof. Richard Bartle Dr Laurissa Tokarchuk Dr Diego Pérez-Liébana Featured Publication(s): Believability Assessment and Modelling in Video Games Predictive models and monte carlo tree search: A pipeline for believable agents Discrete versus Ordinal Time-Continuous Believability Assessment Trace it like you believe it: Time-continuous believability prediction Studying believability assessment in racing games PAGAN for Character Believability Assessment Rolling Horizon Co-evolution in Two-player General Video Game Playing Themes Creative Computing - Previous Next

< Back 'Did you hear that?' Learning to play video games from audio cues Link Author(s) RD Gaina, M Stephenson Abstract More info TBA Link

< Back Lessons from testing an evolutionary automated game balancer in industry Link Author(s) M Morosan, R Poli 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 Deepdream is blowing my mind Link Author(s) M Akten Abstract More info TBA Link

< Back Simultaneous multi-view object recognition and grasping in open-ended domains Link Author(s) H Kasaei, S Luo, R Sasso, M Kasaei Abstract More info TBA Link

< Back Dr Sebastian Berns Queen Mary University of London iGGi Alum Sebastian is a designer and researcher working on use-inspired fundamental research in generative machine learning for creative and artistic applications. Sebastian holds a master’s degree in artificial intelligence and has a background in visual communications. He has worked several years as an independent graphic and type designer with a specialisation in web development. His design work has been awarded national and international design prizes. A description of Sebastian's research: "Generative machine learning methods are trained on raw data, modelling the primary patterns that constitute typical examples. They enable the production of high-quality artefacts in very complex domains and provide useful models for generative systems, in particular in the visual arts and video games. However, modelling a training data distribution perfectly is less valuable for applications in art production and video games. In particular, our analysis of the use of generative models in visual art practices motivates the need to increase the output diversity of generative models. In my dissertation, I focus on diversity in generative machine learning for visual arts and video games. Our findings benefit the application of generative models in generative systems, quality diversity search, art production and video games. Rather than a ‘ground truth’ that needs to be modelled perfectly, we argue that training datasets are merely a limited snapshot of a complex world with inherent biases. To be useful for applications in visual arts and video games, generative models require higher output diversity. Relatedly, higher generative diversity benefits efforts of equity, diversity and inclusion by reducing harmful biases in generative models." s.berns@qmul.ac.uk Email Mastodon http://www.sebastianberns.com/ Other links Website LinkedIn BlueSky https://github.com/sebastianberns Github Featured Publication(s): Not All the Same: Understanding and Informing Similarity Estimation in Tile-Based Video Games Towards Mode Balancing of Generative Models via Diversity Weights Increasing the Diversity of Deep Generative Models Active Divergence with Generative Deep Learning--A Survey and Taxonomy Automating Generative Deep Learning for Artistic Purposes: Challenges and Opportunities Expressivity of Parameterized and Data-driven Representations in Quality Diversity Search First experiments in the automatic generation of pseudo-profound pseudo-bullshit image titles Generative Search Engines: Initial Experiments Adapting and Enhancing Evolutionary Art for Casual Creation. Creativity Theatre for Demonstrable Computational Creativity Bridging Generative Deep Learning and Computational Creativity NEST 2.18. 0 Active Divergence with Generative Deep Learning--A Survey and Taxonomy Themes Creative Computing - Previous Next

< Back Optimising level generators for general video game AI Link Author(s) O Drageset, MHM Winands, RD Gaina, D Perez-Liebana Abstract More info TBA Link

< Back How Players Learn Team-versus-Team Esports: First Results from A Grounded Theory Study Link Author(s) J Hesketh, CS Deterding, J Gow Abstract More info TBA Link

< Back TileAttack Link Author(s) C Madge Abstract More info TBA Link

< Back A generalized framework for self-play training Link Author(s) D Hernandez, K Denamganaï, Y Gao, P York, S Devlin, S Samothrakis, ... Abstract More info TBA Link

< Back Following the leader in multiplayer tabletop games Link Author(s) J Goodman, D Perez-Liebana, S Lucas Abstract More info TBA Link