Browsing by Author "Oulasvirta, Antti, Prof., Aalto University, Department of Information and Communications Engineering, Finland"

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Human-in-the-Loop Design Optimization
(2023) Liao, Yi-Chi
School of Electrical Engineering | Doctoral dissertation (article-based)
This dissertation presents novel computational methods and investigations to enable human-in-the-loop optimization (HILO) for a wider range of realistic applications, allowing designers to efficiently explore the design space of practical problems. Designing effective interaction techniques requires careful consideration of various parameters, that significantly impact user experience and performance. However, optimizing these parameters can be challenging due to the large, multi-dimensional design space, the unclear relationship between parameter settings and user performance, and the complexity of balancing multiple design objectives. Traditionally, designers perform manual optimization via iterative design processes, which can be time-consuming, and effortful, and does not guarantee the best outcome. HILO emerged as a more principled solution for design optimization, using a computational optimizer to intelligently select the next design instance for user testing. Despite some examples of HILO in the human-computer interaction (HCI) field, its application scope is limited to a single objective and for a single user. How to extend it for handling multi-objective problems, optimizing for a population, and supporting physical interfaces has remained unclear. Furthermore, conducting HILO does not eliminate the costs arising from human involvement, and practitioners have been reluctant to embrace a technique whose positive and negative qualities are poorly understood. This dissertation presents a set of computational methods and investigations that speak to these challenges. Pareto-frontier learning is utilized to handle multi-objective design tasks, and I introduce novel extensions for practical solutions of group-level Bayesian optimization. To reduce the effort and time in prototyping, I propose using physical emulation to render physical design instances, enabling HILO to be applied to the design of physical interactions. The dissertation presents user experiments and a design workshop conducted to enrich the understanding of Bayesian optimization-supported design processes' strengths and limitations. Finally, in light of the resource-intensive nature of user studies, a simulation-based optimization framework is proposed whereby artificial users evaluate design instances. With the ultimate goal of expanding HILO's utility in realistic and general design tasks, this dissertation opens new directions for future HILO research. One important path for exploration involves more advanced optimization techniques, such as methods that enable greater efficiency and support a high-dimensional design space. The project also spotlights the value of investigating better human-machine collaboration mechanisms in design optimization such that the designers can steer the optimization as required or fine-tune the suggestions proposed by the optimizer. Lastly, simulation-based optimization methods require further validation, and developing human-like models will be a crucial next step.