Semantic Difference Method for Artificial Intelligence Assisted Cruise Ship Cabin Design

Abstract: In the traditional design process, for determining the form of a single project or product, designers often need to create hundreds to thousands of sketches. However, in the field of cruise ship interior decoration design, this issue becomes more complex. Each large cruise ship has thousands of rooms. Due to restrictions on room types and user needs, they each require unique interior decoration plans; at the same time they must maintain consistency with the theme of the entire cruise ship. Therefore, in the process of cruise ship interior design, designers often have to draw thousands of detailed interior design drawings. Facing such a huge amount work task under an industrial age background undoubtedly puts great pressure on designers.As we gradually step into the era of artificial intelligence, generative AI technology continues to achieve significant breakthroughs, such as ChatGPT and Midjourney, which can generate required images or text information based on keywords or prompts. Designers leverage these advanced technologies like AIGC to generate images by inputting a few keywords and can thus create vast image resources to provide them with abundant sources of inspiration. This method significantly enhances the design efficiency and creative quality of designers in an academically translated way.However, stark differences exist between the comprehension of images by artificial intelligence generative technology and designers. Machines struggle more with understanding adjectives than other types of words, such as 'luxurious' or 'classical', which are highly associated with people's cognitive preferences. Designers often find in using AIGC that the system-generated images do not fully meet their anticipated needs; a large proportion of the images generated by AIGC require subsequent adjustments and cannot be directly applied. This raises the issue at focus in this research: there is a discrepancy between human cognition of vocabulary and machine understanding.Therefore, in this context, we have chosen cruise ship interior design as the case study for our research. From a designer's perspective, we delve deeply into understanding the cognitive discrepancies that exist between artificial intelligence and humans. Firstly, we thoroughly examine the differences in understanding adjectives between AI and humans, incorporating existing research results to empirically construct a semantic evaluation scale specifically for cruise ship-related terminology. This scale aims to describe, measure and analyse these cognitive discrepancies and ultimately provide an effective tool to assist cruise ship interior designers conduct innovative designs more accurately and efficiently.The method adopted in this research is the integration of Kansei engineering and AI generative technology AIGC applied to the relatively novel field of cruise ship interior design, combined comprehensively with Semantic Differential (SD) approach. The SD method can use adjectives to reflect group preferences for images and environments to a large extent. We have selected the most representative words associated with cruise ship style through analysis, collected typical images, and categorized them. Then we utilized the SD method to conduct detailed analysis on accumulated vocabulary and pictures, creating descriptive statistical reports which ultimately form a language evaluation scale specifically reflecting elements and concepts characteristic of cruise ships.In theoretical terms, this study employs the method of Kansei Engineering to construct a semantic evaluation scale specifically for cruise interior design, thereby further enriching the evaluation framework in this field. In practical terms, it introduces AIGC as an aid to stimulate designers' innovative thinking and provide reference image inspiration to enhance their overall design efficiency.

Keywords: Semantic Difference Method, Artificial Intelligence Generated Content, Cruise Ship Cabin Design

DOI: 10.54941/ahfe1005576

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