Creative Computing for Bespoke Ideation

Abstract

Today, idea generation is an extremely important activity for both academic researchers and industrial groups. A considerable number of applications and research studies have been made in the past years in order to increase the effectiveness of idea making process. However, there is lack of efforts on making machine generated ideas novel, surprising and valuable. This research uses concept machine ideation for machinery creative ideas generation and aims to discuss the realistic of bespoke machines ideation, which is simplified as bespoke ideation in the main text. After carefully reviewed the current situation of human ideation, it raises challenge issue to be addressed in this paper. Then, it reviews representative human ideation cases and summarises an overview process for general human ideation. Moreover, it simulates the overview process into a machine ideation process. Next, related concepts and techniques are discussed including Creative Computing, new idea, and creativity. Based on the discussions and the simulated machine ideation process, this paper provides a Creative Computing method to achieve bespoke ideation. Then, detailed techniques and rules that needed to be designed and applied are planned and explained for all the phases to fulfil the machine ideation approach as bespoke approach. Furthermore, a prototype of Research Topics Generation System (RTGS) is developed as a bespoke ideation system to illustrate the proposed approach as well as prove the approach's feasibility, in which a set of new research topics on a designated domain is generated to inspire further studies. The main contribution of this paper is the proposed bespoke ideation approach, by which machine ideation is possible to be achieved in reality.

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“…For example, Boden's theory can be transferred into research on conceptual space. Changing the conceptual space of the dataset can create novel data units based on the creative computing theory [32][33][34]. Therefore, new conceptual space can be generated by combining unrelated data based on the combinational method, changing principle components of data units based on the transformational method, and reducing principle components by using exploratory-based methods.…”

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