Search Results

Automatic Evaluation of Tabletop Games Theme Game AI Project proposed & supervised by Diego Pérez-Liébana To discuss whether this project could become your PhD proposal please email: diego.perez@qmul.ac.uk < Back Automatic Evaluation of Tabletop Games Project proposal abstract: Modern Tabletop games are complex environments that combine multiple components: cards, tokens, boards, mechanics and rules. The amount, type and parameter values for each of these components affect the playability and balance of the game, influencing how the game is perceived by players and what strategies are better to win. This project proposal aims to research and develop methods to accurately evaluate the impact of each of these components in different aspects of gameplay, such as dominant strategies and game balance. In particular, the focus is set in large and complex games where it is computationally unaffordable to evaluate old or new content by playing the full game. The outcome of this project can lead not only to advance the state of the art in automatic evaluation techniques for existing games, but also to evaluate the impact of new components or mechanics in a game in development. Supervisor: Diego Pérez-Liébana Based at:

< Back Casual Creators in the Wild: A Typology of Commercial Generative Creativity Support Tools Link Author(s) E Petrovskaya, CS Deterding, S Colton Abstract More info TBA Link

< Back Social Interactions in Immersive Virtual Environments: People, Agents, and Avatars Link Author(s) GC Dobre Abstract More info TBA Link

< Back Intrinsic elicitation: A model and design approach for games collecting human subject data Link Author(s) D Gundry, S Deterding 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. HandCircus

< Back Interactive generation of calligraphic trajectories from Gaussian mixtures Link Author(s) D Berio, FF Leymarie, S Calinon Abstract More info TBA Link

< Back Dissociating haptic feedback from physical assistance does not improve motor performance Link Author(s) E Ivanova, N Pena-Perez, J Eden, Y Yip, E Burdet Abstract More info TBA Link

< Back The State of the Art in Procedural Audio Link Author(s) D Menexopoulos, P Pestana, J Reiss Abstract More info TBA Link

< Back UCL+ Sheffield at SemEval-2016 Task 8: Imitation learning for AMR parsing with an alpha-bound Link Author(s) J Goodman, A Vlachos, J Naradowsky Abstract More info TBA Link

< Back Archaeological Gameworld Affordances: A Grounded Theory of How Players Interpret Environmental Storytelling Link Author(s) F Smith Nicholls, M Cook Abstract More info TBA Link

< Back The Graph Cut Kernel for Ranked Data Link Author(s) M Conserva, MP Deisenroth, KSS Kumar Abstract More info TBA Link

Novel video narrative from recorded content Theme Creative Computing Project proposed & supervised by Nick Pears To discuss whether this project could become your PhD proposal please email: nick.pears@york.ac.uk < Back Novel video narrative from recorded content Project proposal abstract: In order to stimulate interest and engagement in games, it is important to give players a wide variety of video content that can provide scenario variations each time they engage with the game. However, creating a large volume of diverse video content manually is expensive and time consuming. This project aims to generate novel video narratives from recorded content with minimal human intervention. This requires automatic visual scene understanding that generates auto tagging of scene content and scene actions, either on a frame-by-frame or short clip basis. As well as understanding frame content, action segmentation strategies will be developed and evaluated. This will enable construction of short novel video narratives - for example, from a manually-defined storyline. Deep learning tools and techniques will be employed throughout this project. Supervisor: Nick Pears Based at: