# Configurable Intelligent Design Based on Hierarchical Imitation Models
## 4 Preliminary Results
### AI Commerce  
The commercial application of configurable design aims to address the significant contradiction between the need for product customization (individualization) and the demand for mass standardized production and sales. The objective is to provide businesses with an intelligent add-on tool for existing e-commerce platforms, enabling the generation of optimal offers online [86; 95; 96]. This add-on allows all participants in e-commerce to engage with the platforms, facilitating product profile construction, smart search, active marketing, and other functionalities. Flexible and intelligent ordering capabilities are crucial advantages in today's e-commerce landscape. Typically, an ordered product can be described by several independent basic characteristics, referred to as its own parameters. Based on these parameters, other order characteristics can be calculated, often subject to certain constraints. Users  
Figure 10: Imitation of the optimal blade of an orthogonal wind turbine. Blade orientation effect of lift index: numerical experiment. Interface screenshot [93; 94]. Figure 9: Imitation of the optimal blade of a collinear wind turbine. Blade twist dependence on rotation speed: numerical experiment. Interface screenshot [93; 94]. often prioritize these characteristics over their own parameters when placing an order. This introduces the challenge of translating the order from user-understandable characteristics into manufacturer-friendly terms. Complications arise due to the existence of dependencies and limitations among these characteristics. Additionally, not all user requirements for characteristics can be met by the manufacturer. As a compromise, manufacturers may offer users alternative ordering options with characteristics that closely align with their requested preferences. Generators or constructors of commercial requests are built upon simulation models of products. For instance, the design of vessels (such as vases and utensils) for orders of finished products and/or 3D printing is considered [86; 95]. A comprehensive model is employed, encompassing geometric and weight characteristics of the vessel. The vessel is formed using elements such as ellipsoids, truncated cones, and disks (refer to Figure 11). In addition to parameters determining the geometric contour, specifications for wall thickness, vessel material, specific gravity, and others are provided. The model comprises a total of 36 parameters categorized based on different criteria, including initial-calculated, basic-intermediate, geometric-physical, and technological-user. Configuration examples for product requests are depicted in Figure 12.  
The imitational approach enables the optimization of design to ensure that the product meets the user's requirements while considering the limitations imposed by typical geometric shapes and the manufacturing capabilities for producing ordered objects. The system aids in the classification of offers and orders, providing manufacturers with a better understanding of which technology to employ for production. Additionally, this concept allows designers to explore and develop their innovative ideas, as long as they fall within the system's constraints, including technological, physical, and other limitations. Another example of commercial configurable design is the tour query generator. This system enables the creation of numerous tour options tailored to each user. It also provides travel companies with valuable insights into customer demand, which can influence the offerings in the tourism market and improve the alignment between supply and demand. The tour model considered in this context is relatively straightforward, encompassing parameters such as tour timing and duration, seasonal weather characteristics, apartment size and location, and tour cost (Figure 13). Overall, the model consists of 28 parameters. Figure 11: Scheme of formation of the vessel model [95]. Figure 14 shows an example of an optimized tourist route configuration, using the example of London, obtained by AI generation.