| id |
cdrf2022_263 |
| authors |
Jiaqi Wang and Wanzhu Jiang |
| year |
2022 |
| title |
Demand-Driven Distributed Adaptive Space Planning Based on Reinforcement Learning |
| doi |
https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_23
|
| source |
Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022) |
| summary |
In the second digital turn, the architecture driven by big data logic is gradually shifting from a traditional static entity to an intellective living organism. This paper explores a space planning algorithm that applies reinforcement learning to the multi-agent system to achieve condition adaptability. This algorithm contains an inclusive environment and programmable agents that represent independent spaces. Through reinforcement learning, personalized space needs are quantified as the agent’s Space Schema, which can provide adaptive behavior strategies to adjust volumetric room boundaries. The spatial organization emerges in multi-agent competition, guided by the Negotiation Schema, realizing the dynamic equilibrium of spatial relations and the stable maximization of collective interests. Through real-time interaction and distributed decision-making, this bottom-up method defines a new architectural paradigm that continuously changes based on demands with its high degree of variability, adaptability and evolvability. |
| series |
cdrf |
| email |
|
| full text |
 file.pdf (624,773 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/05/29 14:02 |
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