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SPACE CO-CREATION

R + D

TOPIC

Co-creation:

Space Reconfiguration by Architect & Agent Simulation Based Machine Learning

DESCRIPTION

This research is a manifestation of architectural co-creation between agent simulation based machine learning and an architect’s tacit knowledge. Instead of applying machine learning brains to agents, the author reversed the idea and applied machine learning to buildings. The project used agent simulation as a database, and trained the space to reconfigure itself based on its distance to the nearest agents. To overcome the limitations of machine learning model’s simplified solutions to complicated architectural environments, the author introduced a co-creation method, where an architect uses tacit knowledge to overwatch and have real-time control over the space reconfiguration process. This research combines both the strength of machine learning’s data-processing ability and an architect’s tacit knowledge.

Through exploration of emerging technologies such as machine learning and agent simulation, the author highlights limitations in design automation. By combining an architect’s tacit knowledge with a new generation design method of agent simulation based machine learning, the author hopes to explore a new way for architects to co-create with machines.

YEAR

2022

TEAM

Anni Dai

KEY WORDS

Machine Learning · Agent Simulation · Co-creation · Artificial Intelligence · Space Reconfiguration

CO-CREATION

ROOM SIZE

Room Size is decided by measuring the distance between wall and center of the room constantly. Instead of having room size increase with increasing amount of people and time, the programme adopts human interaction of weight input, that allows live control over room size more than just agent quantity and time. The programme accumulates and average the results.

ROOM HEIGHT

Room Height is associated the agent quantity and amount of time spent in room. Instead of having room height increase with increasing amount of people and time, the programme adopts human interaction of weight input, that allows live control over room size more than just agent quantity and time.The programme accumulates and average the results.

APPLICATION

CONCLUSION

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