A computational approach for combinational creativity in design

Abstract

Creativity is essential for innovative design. As an important topic in computational creativity research, how to model and generate creativity has sparked much interest. In this paper, combinational creativity is explored to provide potential semantic solutions and to generate visually synthesized solutions based on a given combination. This is achieved by a data-driven module and an artificial intelligence module respectively, which have been implemented as a creativity tool used in a design case study to demonstrate their effectiveness for assisting combinational creativity design.