Addressing adjacency constraints in rectangular floor plans using Monte-Carlo Tree Search

Journal article (2020)

Authors

Feng Shi The Alan Turing Institute, Imperial College London, Amazon.com Inc.
R. K. Soman The Alan Turing Institute, Imperial College London
Ji Hyung Han University of Liverpool
Jennifer K. Whyte The Alan Turing Institute, Imperial College London
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Published Date

2020

Language

English

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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.