  Designers reportedly struggle with design optimization tasks where they are
asked to find a combination of design parameters that maximizes a given set of
objectives. In HCI, design optimization problems are often exceedingly complex,
involving multiple objectives and expensive empirical evaluations. Model-based
computational design algorithms assist designers by generating design examples
during design, however they assume a model of the interaction domain. Black box
methods for assistance, on the other hand, can work with any design problem.
However, virtually all empirical studies of this human-in-the-loop approach
have been carried out by either researchers or end-users. The question stands
out if such methods can help designers in realistic tasks. In this paper, we
study Bayesian optimization as an algorithmic method to guide the design
optimization process. It operates by proposing to a designer which design
candidate to try next, given previous observations. We report observations from
a comparative study with 40 novice designers who were tasked to optimize a
complex 3D touch interaction technique. The optimizer helped designers explore
larger proportions of the design space and arrive at a better solution, however
they reported lower agency and expressiveness. Designers guided by an optimizer
reported lower mental effort but also felt less creative and less in charge of
the progress. We conclude that human-in-the-loop optimization can support
novice designers in cases where agency is not critical.
