Utilizing Artificial Intelligence Techniques in Complex Form Generation

Eskandar, Youstina; Hosny, Samir Sadek; Abdelmohsen, Sherif; Hamza, Hussein

doi:10.21608/erjsh.2023.226325.1204

Utilizing Artificial Intelligence Techniques in Complex Form Generation

Document Type : Review Articles

Authors

¹ Department of Architecture, American University in Cairo, Cairo, Egypt

² Department of Architectural Engineering, FUE, Future University in Egypt

³ Department of Architecture, Faculty of Engineering, Ain Shams University, Cairo, Egypt

10.21608/erjsh.2023.226325.1204

Abstract

Architects have always aimed at developing more adequate software to capture the complexity of biological self-organizational systems in architectural form generation. Agent-based modeling (ABM) emerged as a promising approach, where agents can exhibit emergent self-organizing behavior. However, the nonlinearity and feedback loops typically associated with natural systems are not quite achieved using traditional ABMs. This paper investigates the integration of reinforcement learning (RL) - an artificial intelligence technique - to develop reinforcement learning ABMs to enhance architectural form generation. Decentralized multi-agent reinforcement learning is proposed as an approach where agents learn complex strategies that maximize rewards through interactions with the environment to model emergent structures that are typical characteristics of natural systems. The paper reviews relevant biological self-organization concepts that inform architectural objectives, surveys previous ABM research, and proposes a RL framework that provides a systematic approach for developing RL models that capture the complexity and adaptability of natural systems, focusing on architectural form generation.

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