We describe an empirical study that explores how users establish and use personal space around large public displays (LPDs). Our study complements field studies in this space by more fully characterizing interpersonal distances based on coupling and confirms the use of on-screen territories on vertical displays. Finally, we discuss implications for future research: limitations of proxemics and territoriality, how user range can augment existing theory, and the influence of display size on personal space.
In recent years, Reinforcement Learning (RL) has seen increasing popularity in research and popular culture. However, skepticism still surrounds the practicality of RL in modern video game development. In this paper, we demonstrate by example that RL can be a great tool for Artificial Intelligence (AI) design in modern, nontrivial video games. We present our RL system for Ubisoft's Roller Champions, a 3v3 Competitive Multiplayer Sports Game played on an oval-shaped skating arena. Our system is designed to keep up with agile, fast-paced development, taking 1-4 days to train a new model following gameplay changes. The AIs are adapted for various game modes, including a 2v2 mode, a Training with Bots mode, in addition to the Classic game mode where they replace players who have disconnected. We observe that the AIs develop sophisticated co-ordinated strategies, and can aid in balancing the game as an added bonus. Please see the accompanying video at https://vimeo.com/466780171 2 for examples. * Work done while at Ubisoft. 2 Password: rollerRWRL2020 34th Conference on Neural Information Processing Systems (NeurIPS 2020),
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