2. Literature Review

2.

that it had on the architectural design process. Evaluation of the several AI systems and techniques that are transforming architectural design and automating some parts of the process. Several research mythologies will be used to collect, analyze and describe the data.

LITERATURE REVIEW

2.1.

Artificial Intelligence

Artificial Intelligence works by simulating human intelligence on computer systems that are programed to think and work like humans. AI works on the principles of learning, reasoning, and perception, it mimics the functioning of human brain and makes connections between packets of information. The application of AI is vast reaching, expanding into several industries and sectors. (FRANKENFIELD & JAKE, 2020) Based on the capabilities of AI, is divided into weak and strong. Weak AI is designed to solve a specific task and its capabilities are limited to that task, for example a system designed to play chess against human opponents. On the other hand, strong AI systems has a human-like analysis capabilities, they are designed to perform more complex and complicated tasks. This system has the ability to work without human intervention, for example the system that operates self-driving cars. (FRANKENFIELD & JAKE, 2020)

2.2. Introduction of AI into architecture

The architectures design process has been evolving ever since designers started designing. As technology improved and new design tools are invented, they are integrated in the architectural process. After the advent of Artificial Intelligence and its integration in several fields, the entire process of architectural design could be reinterpreted along with the role of architects themselves (Retsin, G. 2019). This push could prove as a great opportunity for architects to examine their current design process and make improvements in the way they work to produce even more sophisticated architecture.

As the architectural production line changes and architects start tacking new and old problems, it would be great if designers can get some help in figuring out answers to complex challenges and this is again where Artificial Intelligence comes into play. In some cases, a sophisticated and well evolved AI system can generate design solution much better than humans (Dounis, 2010). Considering cases such as climate change and green designs, humans have done some progress, but the situation is getting worse and AI intervention might be the break though architects were waiting for. AI show great potential as the automated building systems that they operate on set goals such as energy efficiency, comfort, health and productivity in living spaces (Dounis, 2010). And these goals can be set and tweaked in the initial systems. As the system evolves and develops independently, more hard points and goals would be set by the AI systems which may also include points missed out by architects, combination of both would yield a more sustainable architectural design.

As the possibility of generative green design is being discussed, it is important to shift our focus on the current advancements in generative design. Technology to recognize and generated architectural floor plans has been successfully studies and experimented. Generative adversarial network also known as GAN has made this possible (Huang, W., & Zheng, H, 2018). GAN is a framework modeled out of machine learning, it studies similar or identical characteristics and can learn or produce specific design based on data that was fed to the system. Pix2pixHD is a reworked version of GAN which learns image data in pairs and generates new images based on the input (Huang, W., & Zheng, H, 2018). Pix2pixHD was trained by assigning color codes to each aspect of an architecture plan, for example the walkway was assigned red color, bedrooms were lite green, windows were dark red, doors dark green and so on. Several architectural plans were inputted to the machine and the system was able to produce a color-coded plan. This process was then reversed, training the machine to produce architectural plans from color codes. Hence one of the first automated architectural plans where generated by simple inputs. As the system develops, these inputs can be points that architects consider before drawing the architectural plans. This will lead to an AI system that needs only the location where the building needs to be constructed and other variables such as budget, rest of the data will automatically be considered by the computer and a suitable design will be generated. Zoning data, building codes, and disabled design data along with other relevant information can be easily stored in the systems memory for the computer to use every time it designs, this data will be readily available for the AI system as well as for the architect (Malaeb, J., & Ma, W, 2019). Liberating architects from the burden of remembering and searching for standards every time they design and allowing them to focus on tweaking the automatically generated plans by the systems. This will lead to a better workflow and in turn a better design. Changes made by architects to the computer generated plans can be studied and noted by the AI system as references and when faced by a similar satiation in the future, the system will have a better and more effective response than the previous time it design. This will lead to a continuous learning process for the system, learning from previous mistakes and evolving into an even powerful machine.

2.3.

Computers, from design tools to design assistants

The creativity of AI lies in its ability to learn and create variation in data and high computing power of systems available these days, thousands of random and usable variations of the input is produced. This has been supported by an experiment where artificial neural networks were trained based on the detailed configuration of the Roman Corinthian order capitals. The successfully trained artificial neural networks was able to generate 3dimensional variations of the new capital forms based on the given input parameters, these variations were both random and purposeful (Cudzik, J., & Radziszewski, K, 2018). Generative design, bias and architectural style are one of the major points to focus on when exploring the future of AI and architectural interaction. Turning these points into computer understandable language, to train the AI system can turn out to be a challenge. But as the system improves, it might find ways to learn based on the architect’s inputs. Advancements in AI has changed the idea of how we perceive machines. Once