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  • Rinascimento: searching the behaviour space of Splendor

    < Back Rinascimento: searching the behaviour space of Splendor Link Author(s) I Bravi, S Lucas Abstract More info TBA Link

  • Theories, methodologies, and effects of affect-adaptive games: A systematic review

    < Back Theories, methodologies, and effects of affect-adaptive games: A systematic review Link Author(s) M Croissant, G Schofield, C McCall Abstract More info TBA Link

  • Gplayer

    < Back Gplayer Link Author(s) N Pedersen, A Canossa, G Lai Abstract More info TBA Link

  • Investigating sensorimotor contingencies in the enactive interface

    < Back Investigating sensorimotor contingencies in the enactive interface Link Author(s) JK Gibbs, K Devlin Abstract More info TBA Link

  • Dissociating haptic feedback from physical assistance does not improve motor performance

    < 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

  • Andrew Martin

    < Back Andrew Martin Queen Mary University of London iGGi Alum Applications in game development for programming language theory and AI Modern game development is highly iterative. Iteration is usually discussed in terms of a team completing design iterations, but can also be considered at the level of an individual developer attempting to complete a task, or experimenting with some ideas. At this level, the feedback loop provided by the tool becomes critical. Programming environments in particular often have a very poor feedback loop. Programming feedback can be thought of in terms of how quickly and seamlessly the user is able to observe the results of their work. This process is usually plagued with manual tasks and long pauses. It is common that a user will need to recompile, relaunch their program, and then manually recreate whatever state is required to observe the behaviour that they are working on. Frameworks like Elm, React and Vuejs are establishing a new norm of automatic hot-reloading with state preservation. These systems represent a branch of programming language research that is strongly focused on developer experience. In order to improve upon this work for game development, we must overcome the unique challenges that game development entails. Although the systems mentioned are all quite recent, there is a rich vein of research to draw on, which can be traced through dataflow programming, Smalltalk, Erlang, functional-reactive programming, Lisp and more. Predictive completions are considered by many to be a natural next-step in the evolution of live programming environments. An AI programming assistant would propose program fragments as completions or alternatives. The agent may seek to anticipate the user’s intent, or to provide creative suggestions. There is much relevant research in the fields of program synthesis, inductive logic programming, machine learning and genetic programming. One significant problem is how to smoothly and safely integrate a system like this into the user’s workflow. Many of the properties useful for safely enabling live programming features, such as isolation of side-effects, will also permit an AI agent to safely generate and execute code. Andy graduated from Imperial College London with an MEng in Computing in 2011. Following this he worked on game engine tools and technology at a startup called Fen Research, and then as a senior developer at a software consulting firm called LShift. In 2016 he spent six months working as a Research Associate in the Computational Creativity group at Goldsmiths, before starting his PhD. Please note: Updating of profile text in progress Email Mastodon Other links Website LinkedIn Twitter Github Themes Game AI - Previous Next

  • Why Choose You?-Exploring Attitudes Towards Starter Pokémon

    < Back Why Choose You?-Exploring Attitudes Towards Starter Pokémon Link Author(s) Abstract More info TBA Link

  • Lateralization of impedance control in dynamic versus static bimanual tasks

    < Back Lateralization of impedance control in dynamic versus static bimanual tasks Link Author(s) N Pena-Perez, J Eden, I Farkhatdinov, E Burdet, A Takagi Abstract More info TBA Link

  • A generalized framework for self-play training

    < 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

  • Susanne Binder

    < Back Susanne Binder Queen Mary University of London iGGi Manager iGGi Admin iGGi Manager @ QMUL ; alongside Tracy Dancer (iGGi Manager @ UoY) , and supported by Shopna Begum and Helen Tilbrook, she's mostly in charge of making things run at iGGi with particular focus on iGGi-QMUL-specific admin iGGi-QMUL-specific student concerns PR, website and social media industry liaison s.binder@qmul.ac.uk Email https://dizl.de/@sus4nn3b1nd3r/ Mastodon Other links Website https://www.linkedin.com/in/susanne-binder-b1184647/ LinkedIn https://twitter.com/sus4nn3b1nd3r Twitter Github Themes - Previous Next

  • Time to die: Death prediction in dota 2 using deep learning

    < Back Time to die: Death prediction in dota 2 using deep learning Link Author(s) A Katona, R Spick, VJ Hodge, S Demediuk, F Block, A Drachen, ... Abstract More info TBA Link

  • PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games

    < Back PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games Link Author(s) M Balla, GEM Long, D Jeurissen, J Goodman, RD Gaina, ... Abstract More info TBA Link

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