AI-Based Hospital Design Process through Neuro-Symbolic Strategies.

Objectives: Hospitals represent one of the most complex subjects in architectural design. Over time, many hospitals undergo changes in their initial spatial layouts to accommodate evolving needs. This process often presents various challenges and problems, and the absence of an optimal design process for hospitals is a primary contributor to these issues. Artificial intelligence (AI), with its advanced capabilities, can offer highly accurate and rapid solutions. This research aims to present an integrated approach that combines architecture and AI for AI-based hospital design through neuro-symbolic strategies.

Methods: This research employs a theoretical-applied framework, and utilizes a descriptive-analytical method to investigate the role of artificial intelligence in the design process, particularly within hospitals. The data collection methods include a literature review and an examination of texts and studies related to architectural design and AI. Furthermore, by analyzing the data through content analysis, integrated neuro-symbolic strategies were introduced as a comprehensive approach to AI. The final model of the hospital design process, based on this approach, was subsequently presented.

Results: Unified and hybrid techniques are two methods for integrating symbolic and sub-symbolic algorithms within the integrated neuro-symbolic approach. This innovative methodology leverages the strengths of both categories of algorithms while mitigating their respective weaknesses. Among the six methods presented in this paper, the hybrid strategy-method number three (neuro-symbolic) - emerges as the most effective means of achieving an integrated process that merges AI and architecture in hospital design.

Conclusion: In this process, the designer's interventions are minimized, allowing AI to produce the most optimal architectural design for a hospital by leveraging its capabilities.