论文标题
物理上一致的优先贝叶斯优化食物排列
Physically Consistent Preferential Bayesian Optimization for Food Arrangement
论文作者
论文摘要
本文考虑了使用基于计算机图形（CG）的菜肴图像来估算交互式成对比较的用户的首选食物布置的问题。作为餐饮服务行业的要求，我们需要利用域规则来进行布置的几何形状（例如，某些日本菜肴的食品布局让人联想到山脉）。但是，这些规则是定性和模棱两可的。估计的结果可能是身体上不一致的（例如，每种食物在物理上会干扰，并且排列变得不可行）。为了解决这个问题，我们建议在物理上一致的优先贝叶斯优化（PCPBO），作为一种获得满足域规则的物理可行和优先安排的方法。 PCPBO采用了双层优化，该优化结合了基于物理模拟的优化和基于偏好的贝叶斯优化（PBBO）。我们的实验结果证明了PCPBO对模拟和实际人类用户的有效性。
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.
