Solution to Resolve Cognitive Ambiguity in Interactive Customization of Product Shape
- https://doi.org/10.2991/ijcis.d.200511.001How to use a DOI?
- User customization, Decision-making, Product shape design, Cognitive ambiguity, Interactive genetic algorithm
Interactive genetic algorithms have been used in a wide variety of applications and extensively developed to facilitate the personalization and customization of products for users. However, the ambiguity effect or cognitive ambiguity of users during the product customization process will affect the effects of the final customized product. Here, we first deconstructed the ambiguity effect into cognitive ambiguity during early decision-making and that during the decision-making process. A spatial mapping strategy that involves “text-image-symbol” and a clustering strategy were then proposed to mitigate cognitive ambiguity in the two different stages, respectively. Then, a specific application example—“Chinese vase design” was studied. Based on the proposed strategy and interactive genetic algorithm, a prototype of computer-aided design system for product modeling was developed by MATLAB software. Then, 10 users were invited to experiment at three different cases (not using the proposed strategy, using spatial mapping strategy, using spatial mapping strategy and scheme clustering strategy). The experiment results have verified that the spatial mapping strategy was efficient to solve cognitive ambiguity during early decision-making, and clustering strategy was efficient to solve cognitive ambiguity during the decision-making process.
- © 2020 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Dong Zeng AU - Zhuan Zhou AU - Maoen He AU - Chaogang Tang PY - 2020 DA - 2020/05 TI - Solution to Resolve Cognitive Ambiguity in Interactive Customization of Product Shape JO - International Journal of Computational Intelligence Systems SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.d.200511.001 DO - https://doi.org/10.2991/ijcis.d.200511.001 ID - Zeng2020 ER -