  This paper considers the problem of estimating a preferred food arrangement
for users from interactive pairwise comparisons using Computer Graphics
(CG)-based dish images. As a foodservice industry requirement, we need to
utilize domain rules for the geometry of the arrangement (e.g., the food layout
of some Japanese dishes is reminiscent of mountains). However, those rules are
qualitative and ambiguous; the estimated result might be physically
inconsistent (e.g., each food physically interferes, and the arrangement
becomes infeasible). To cope with this problem, we propose Physically
Consistent Preferential Bayesian Optimization (PCPBO) as a method that obtains
physically feasible and preferred arrangements that satisfy domain rules. PCPBO
employs a bi-level optimization that combines a physical simulation-based
optimization and a Preference-based Bayesian Optimization (PbBO). Our
experimental results demonstrated the effectiveness of PCPBO on simulated and
actual human users.
