Shi, Feng, Soman, Ranjith K, Han, Ji and Whyte, Jennifer K
(2020)
Addressing adjacency constraints in rectangular floor plans using Monte-Carlo Tree Search.
AUTOMATION IN CONSTRUCTION, 115.
103187-.
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An efficient approach to address adjacency constraints.pdf - Author Accepted Manuscript Download (1MB) | Preview |
Abstract
Manually laying out the floor plan for buildings with highly-dense adjacency constraints at the early design stage is a labour-intensive problem. In recent decades, computer-based conventional search algorithms and evolutionary methods have been successfully developed to automatically generate various types of floor plans. However, there is relatively limited work focusing on problems with highly-dense adjacency constraints common in large scale floor plans such as hospitals and schools. This paper proposes an algorithm to generate the early-stage design of floor plans with highly-dense adjacency and non-adjacency constraints using reinforcement learning based on off-policy Monte-Carlo Tree Search. The results show the advantages of the proposed algorithm for the targeted problem of highly-dense adjacency constrained floor plan generation, which is more time-efficient, more lightweight to implement, and having a larger capacity than other approaches such as Evolution strategy and traditional on-policy search.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Floor plan generation, Highly-dense adjacency and non-adjacency constraint, Algorithm, Off-policy Monte-Carlo tree search, Reinforcement learning, Generative design |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 18 Mar 2020 08:44 |
| Last Modified: | 15 Mar 2024 12:18 |
| DOI: | 10.1016/j.autcon.2020.103187 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3079549 |
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