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公开(公告)号:US20230281349A1
公开(公告)日:2023-09-07
申请号:US17687535
申请日:2022-03-04
Applicant: AUTODESK, INC.
Inventor: Yi WANG , Mehdi NOURBAKHSH , Dale ZHAO
Abstract: In various embodiments, an intent-driven layout application automatically generates design for floor spaces. The intent-driven layout application generates a logic formula based on a statement of a design intent and at least one fuzzy geometric predicate. The intent-driven layout application computes, for a first spatial object, a set of desirability values for a set of candidate placements within a first design based on the logic formula. Based on the set of desirability values, the intent-driven layout application selects a first candidate placement from the set of candidate placements. Subsequently, the intent-driven layout application generates a second design based on the first design, where the first spatial object has the first candidate placement within the second design.
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公开(公告)号:US20220083703A1
公开(公告)日:2022-03-17
申请号:US17071992
申请日:2020-10-15
Applicant: AUTODESK, INC.
Inventor: Yi WANG , Mehdi NOURBAKHSH
Abstract: One embodiment of the present invention sets forth a technique for performing machine learning. The technique includes applying one or more placement rules to a floorplan of a building to generate a set of candidate column locations in the floorplan. The technique also includes selecting, using a first reinforcement learning (RL) agent, one or more column locations from the set of candidate column locations based on a structural stability of the one or more column locations. The technique further includes outputting the floorplan that includes the one or more column locations as a structural design for the building.
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公开(公告)号:US20240202381A1
公开(公告)日:2024-06-20
申请号:US18595179
申请日:2024-03-04
Applicant: AUTODESK, INC.
Inventor: Yi WANG , Medhi NOURBAKHSH
Abstract: One embodiment of the present invention sets forth a technique for performing machine learning. The technique includes applying one or more placement rules to a floorplan of a building to generate a set of candidate column locations in the floorplan. The technique also includes selecting, using a first reinforcement learning (RL) agent, one or more column locations from the set of candidate column locations based on a structural stability of the one or more column locations. The technique further includes outputting the floorplan that includes the one or more column locations as a structural design for the building.
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