Sampling Humans for Optimizing Preferences in Coloring Artwork
This work addresses a domain-specific problem for artists or designers, but it is incremental as it adapts an existing method.
The paper tackles the problem of optimizing preferences in artwork coloring without explicit metrics by adapting a Bayesian optimization strategy to handle ties, and discusses challenges encountered when humans use this method.
Many circumstances of practical importance have performance or success metrics which exist implicitly---in the eye of the beholder, so to speak. Tuning aspects of such problems requires working without defined metrics and only considering pairwise comparisons or rankings. In this paper, we review an existing Bayesian optimization strategy for determining most-preferred outcomes, and identify an adaptation to allow it to handle ties. We then discuss some of the issues we have encountered when humans use this optimization strategy to optimize coloring a piece of abstract artwork. We hope that, by participating in this workshop, we can learn how other researchers encounter difficulties unique to working with humans in the loop.