Automatic design quality evaluation using graph similarity measures

Abstract

This paper contributes to the problem of assisting the designer in dealing with evaluating the quality of a design. Especially, spatial relationships and arrangements of components within a design are explicitly dealt with. Hierarchical graphs are used as the design representation to enable capturing different ways components can be related by taking into account the fact that a component can form a part of another one. As the human evaluation is often based on the experience gained from seeing and analyzing many designs a similar approach is proposed in this paper. This approach uses methods drawn from machine learning, in particular kernels for structured data. Kernel functions are used to calculate similarity of new designs to other designs for which the evaluation is known thus simulating the process of learning from experience. The proposed approach is illustrated by experimental results obtained for the task of floor layout design.