AI-Based Use-Pattern Generative Hybrid Spaces for Indoor and Outdoor Activities

Abstract

This research explores machine learnt use-patter of human activities in outdoor and indoor spaces. It proposes scenarios to be used through Generative Adversarial Network for AI-Based use-pattern generative spaces. The study investigates futuristic AI-based life styles and its impact on the architectural spaces and clusters agglomeration. Clusters algorithmic design includes cellular automata and fractals in regards of stable null space as green nodes. Machine learnt use-pattern from daily activities incorporates a dynamic-responsive pixelation of spaces. The research manifest machine learnt algorithmic design and demonstrates IOT generative responsive spaces. A hybrid space of extended spatial reality that complies as well architectural boundaries has resulted using cellular responsive units/cells to include an extended reality (XR) part that supply the space with extended multiple activities. This hybrid space acts as a virtual and a real space that accommodates various functionalities with flexibilities relying on IOT, AI-based and XR technologies.