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- W3175267858 abstract "With emerging technologies like robots, mixed-reality systems or mobile devices, machine-provided capabilities are increasing, so is the complexity of their control and display mechanisms. To address this dichotomy, we propose optimal control as a framework to support users in achieving their high-level goals in human-computer tasks. We reason that it will improve user support over usual approaches for adaptive interfaces as its formalism implicitly captures the iterative nature of human-computer interaction. We conduct two case studies to test this hypothesis. First, we propose a model-predictive-control-based optimization scheme that supports end-users to plan and execute robotic aerial videos. Second, we introduce a reinforcement-learning-based method to adapt mixed-reality augmentations based on users’ preferences or tasks learned from their gaze interactions with a UI. Our results show that optimal control can better support users’ high-level goals in human-computer tasks than common approaches. Optimal control models human-computer interaction as a sequential decision problem which represents its nature and, hence, results in better predictability of user behavior than for other methods. In addition, our work highlights that optimization- and learning-based optimal control have complementary strengths with respect to interface adaptation." @default.
- W3175267858 created "2021-07-05" @default.
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- W3175267858 date "2021-01-01" @default.
- W3175267858 modified "2023-10-18" @default.
- W3175267858 title "Optimal Control to Support High-Level User Goals in Human-Computer Interaction" @default.
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- W3175267858 doi "https://doi.org/10.1007/978-3-030-82681-9_2" @default.
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