Search Results

< Back Game AI How might we use novel AI techniques including machine learning and decision search to create more effective and engaging game agents and understand human behaviour in games? Project areas include: AI agents that play games using Deep Reinforcement Learning and Statistical forward planning AI agents as non-player characters General Video Game AI - AI that can be transferred between video games AI agents for game balancing and Quality Assurance testing << Previous Theme page Next Theme page >> iGGi >>> People <<< relevant to this Theme: Michael Aichmüller iGGi Alum ​ Game AI, Applied Games Read More Dr Martin Balla iGGi Alum Available for post-PhD position Game AI Read More Matt Bedder iGGi Alum ​ Game AI Read More Toby Best iGGi PG Researcher Available for placement Game AI, Design & Development, Player Research Read More Dr Memo Akten iGGi Alum ​ Game AI Read More Dr Mathieu Barthet Supervisor ​ Game AI, Game Audio Read More Prof. David Beer Supervisor ​ Player Research, Applied Games, Creative Computing, Game Data, Game AI Read More Dr Adrian Bors Supervisor ​ Game AI Read More Dr Myat Aung iGGi Alum ​ Game AI Read More Prof. Richard Bartle Supervisor ​ Design & Development, Game AI, Player Research Read More Dr Daniel Berio iGGi Alum ​ Game AI Read More Dr Ivan Bravi iGGi Alum ​ Game AI, Player Research Read More Load More iGGi People working in this Theme iGGi >>> Publications <<< relevant to this Theme: Formal Constraints and Creativity: Connecting Game Jams, Dogma ’95, the Demo Scene, OuBaPo, and Renga poets G Lai, I Vecchi Games and Culture, 2024 Gorm Lai View Details Playing NetHack with LLMs: Potential & Limitations as Zero-Shot Agents D Jeurissen, D Perez-Liebana, J Gow, D Cakmak, J Kwan arXiv preprint arXiv:2403.00690, 2024 Dominik Jeurissen View Details VDSC: Enhancing Exploration Timing with Value Discrepancy and State Counts M Captari, R Sasso, M Sabatelli arXiv preprint arXiv:2403.17542, 2024 Remo Sasso View Details Applying and Visualising Complex Models in Esport Broadcast Coverage A Pedrassoli Chitayat, F Block, JA Walker, A Drachen Proceedings of the 2024 ACM International Conference on Interactive Media Experiences, 2024 Alan Pedrassoli Chitayat View Details Player Expectations of Strategy Game AI D Gomme University of Essex, 2024 Dr Daniel Gomme View Details How Could They Win? An Exploration of Win Condition for Esports Narratives AP Chitayat, FO Block, JA Walker, A Drachen Annual Symposium on Computer-Human Interaction in Play (CHI PLAY 2024), 2024 Alan Pedrassoli Chitayat View Details Load More iGGi Publications for this Theme Previous Next

< 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 https://twitter.com/sebastianberns Twitter 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 The State of the Art in Procedural Audio Link ​ Author(s) D Menexopoulos, P Pestana, J Reiss Abstract ​ More info TBA ​ Link

< Back ​ Prof. Nick Pears University of York ​ Supervisor ​ ​ Nick Pears is a Professor of Computer Vision in York’s Vision, Graphics and Learning (VGL) research group. He works on statistical modelling of 3D shapes, with an emphasis on the human face and head. The Liverpool-York Head Model and the associated Headspace training set has been downloaded by over 100 research groups internationally, with the Universal Head Model being downloaded by 50 research groups. His most recent work with his PhD students has focused on semantic disentanglement of 3D images and how to make autonomous vehicles safer and more trustworthy when using computer vision systems. He is assessor for many PhDs including construction of generative models for novel video content using adversarial deep learning techniques. ​ nick.pears@york.ac.uk Email Mastodon https://www-users.cs.york.ac.uk/np7/ Other links Website https://www.linkedin.com/in/nick-pears-90970312/ LinkedIn Twitter Github ​ ​ Themes Creative Computing Game AI - Previous Next

< Back ​ Prof. Marian Ursu Goldsmiths ​ Supervisor ​ ​ Marian Ursu has a first degree in Computer Science, a PhD in Artificial Intelligence, and has worked over the past twenty years in the development of new forms of mediated expression and interaction, in the space of convergence of digital technology with creative practice. He worked at Goldsmiths, University of London, pioneering “creative computing”, a term denoting a fundamental link between digital technologies, the arts and media. At York, he led the development of the Interactive Media subject area in the department of Theatre Film Television and Interactive Media, inherently interdisciplinary, building on Computer Science, User Experience Design, Media Practice and Cultural Studies. He is a co-founder and the Director of the Digital Creativity (DC) Labs ( https://digitalcreativity.ac.uk ), a centre of excellence in impact-driven research in creativity for games, narrative media and the rich space of media convergence that lies in between, and Co-Director of XR Stories ( https://xrstories.co.uk ), a creative industries partnership working across film, TV, games, media arts, heritage, advertising and technology to champion a new future in storytelling, in which he leads on Research and Development. His personal research is situated in the area of narrative experiences in scree media – shared screens (film, TV), personal screens (games, social media), stories in VR, XR narrative experiences – drawing from and building on established narrative art-forms and media including film and TV, radio, theatre, and opera. One of his key research objective is to explore the creative process that emerges in dialogue between humans and machines (AI). On one hand, this is necessary for the authoring of more complex narrative experiences that truly exploit the affordances of interactive and immersive digital media technologies. On the other hand, this is a yet poorly untapped space of opportunities, potentially conducive to significant findings. He is particularly interested in supervising students interested in exploring creativity in dialogue with AI and/or the development of novel narrative experiences, in topics including: Conceptualising the space of interactive storytelling Developing authoring tools and techniques for interactive storytelling Creating new forms of narrative engagement Analysing the concept of creativity in interactive media which emerges in conversation with AI Research themes: Narrative Games; Narrative Experiences Storytelling with Convergent Media Object-Based Media Computational Creativity Live mediated experiences (performance, sports, esports) Entertainment media and mental health Games and Theatre ​ marian.ursu@york.ac.uk Email Mastodon Other links Website https://www.linkedin.com/in/marianursu/?originalSubdomain=uk LinkedIn https://twitter.com/@MarianF_Ursu Twitter Github ​ ​ Themes Creative Computing Player Research - Previous Next

< Back Is a robot needed to modify human effort in bimanual tracking? Link ​ Author(s) N Pena-Perez, J Eden, E Ivanova, E Burdet, I Farkhatdinov Abstract ​ More info TBA ​ Link

< Back Actions, not gestures: contextualising embodied controller interactions in immersive virtual reality Link ​ Author(s) J Ratcliffe, N Ballou, L Tokarchuk Abstract ​ More info TBA ​ Link

< Back First experiments in the automatic generation of pseudo-profound pseudo-bullshit image titles Link ​ Author(s) S Colton, S Berns, BP Ferrer 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 ​ Dr Mike Cook ​ ​ Supervisor ​ ​ Mike is a Senior Lecturer at King's College London where he leads research into automated game design, computational creativity, and the theory and practice of generative systems. ​ mike@possibilityspace.org Email Mastodon https://www.possibilityspace.org/ Other links Website LinkedIn https://twitter.com/mtrc Twitter Github ​ ​ Themes Creative Computing Design & Development Game AI - Previous Next

< Back Recurrent Neural-Linear Posterior Sampling for Nonstationary Contextual Bandits Link ​ Author(s) A Ramesh, P Rauber, M Conserva, J Schmidhuber Abstract ​ More info TBA ​ Link

< Back ​ Dr Piers Williams University of Essex ​ iGGi Alum ​ ​ Partial Observability as a game mechanic There is a wide variety of different types of games, each providing its own unique challenge to artificial intelligence. Not all games provide full access to the environment, creating interest and difficulty by hiding particular pieces of information from the player. Other types of game expect teamwork from the players rather than being solely adversarial. Some games use both restrictions, and it is this type of game that this thesis concentrates on. Piers graduated from the University of Essex with an MSc in Computer Science. His interests lie in the field of Artificial Intelligence and in particular Multi-Agent Systems. Please note: Updating of profile text in progress ​ Email Mastodon Other links Website LinkedIn Twitter Github ​ Featured Publication(s): Hexboard: A generic game framework for turn-based strategy games Evaluating and Modelling Hanabi-Playing Agents Monte carlo tree search applied to co-operative problems The 2018 hanabi competition Artificial intelligence in co-operative games with partial observability Ms. Pac-Man Versus Ghost Team CIG 2016 Competition Cooperative games with partial observability Themes Game AI - Previous Next