Floorplan Generation via Monte Carlo Tree Search Optimization
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Solution Overview
Problem
Current methods for generating residential floorplans, especially using deep learning and meta-heuristic algorithms, face challenges with limited data generalization, adaptability to regional differences, and inefficiency in optimizing complex spatial layouts due to large search spaces and local optima issues.
Innovation Solution
The method employs Monte Carlo tree searches to optimize the deployment of rooms in a floorplan by constructing a search graph, pruning unnecessary options, and updating weights based on an evaluation function to efficiently explore feasible solutions and adapt to design conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If deep learning is used to generate floorplans, then automation is improved, but data generalization ability deteriorates when training data is limited
Solution Approach 1:
The patent introduces a reward model as an intermediary between the floorplan generation model and the evaluation criteria. This reward model, trained on expert-designed floorplans, serves as a mediator that guides the generation process without requiring the main model to be trained on large amounts of diverse data. The reward model encapsulates design knowledge and constraints, enabling the system to generate adaptable floorplans across different regions and requirements while maintaining automation.
2Reliability
If meta-heuristic algorithms are used to optimize building performance, then optimization capability is improved, but search efficiency deteriorates due to large search spaces and local optima
Solution Approach 1:
The patent implements a feedback mechanism through the reward model that evaluates generated floorplans and provides guidance for improvement. The Monte Carlo Tree Search (MCTS) algorithm uses this feedback to iteratively refine floorplan designs, learning from evaluation results to avoid local optima. The feedback loop between generation, evaluation, and refinement enables efficient navigation of the search space while maintaining high optimization capability.
Solution Approach 2:
The patent employs dynamic search strategies where the MCTS algorithm adaptively adjusts its exploration and exploitation behavior based on the current search state and reward feedback. The algorithm dynamically focuses computational resources on promising regions of the search space while avoiding areas that have already been adequately explored, thereby improving search efficiency without sacrificing optimization capability.
3Measurement precision
If Monte Carlo tree searches are performed multiple times, then search thoroughness is improved, but computational time increases
Solution Approach 1:
The patent performs multiple Monte Carlo tree searches but with strategically limited depth and breadth in each iteration. Rather than exhaustively exploring all possible floorplan configurations, the system performs multiple partial searches that collectively cover the search space more thoroughly than a single exhaustive search would allow within the same time budget. This approach balances search thoroughness with computational time by distributing effort across multiple focused iterations.
Data Source
AI summary
A method for residential unit floorplan generation, includes obtaining a current status of a floorplan and room information of one or more rooms to be deployed, according to the current status of the floorplan and the room information of the one or more rooms to be deployed, performing multiple Monte Carlo tree searches to obtain a search result, and deploying a room of the one or more rooms to be deployed according to the search result to update the floorplan.