Automating Layout Synthesis with Constructive Preference Elicitation
Abstract
Layout synthesis refers to the problem of arranging objects subject to design preferences and structural constraints. Applications include furniture arrangement, space partitioning (e.g. subdividing a house into rooms), urban planning, and other design tasks. Computer-aided support systems are essential tools for architects and designers to produce custom, functional layouts. Existing systems, however, do not learn the designer’s preferences, and therefore fail to generalize across sessions or instances. We propose addressing layout synthesis by casting it as a constructive preference elicitation task. Our solution employs a coactive interaction protocol, whereby the system and the designer interact by mutually improving each other’s proposals. The system iteratively recommends layouts to the user, and learns the user’s preferences by observing her improvements to the recommendations. We apply our system to two design tasks, furniture arrangement and space partitioning, and report promising quantitative and qualitative results on both. Code related to this paper is available at: https://github.com/unitn-sml/constructive-layout-synthesis/tree/master/ecml18.
Keywords
Constructive learning Preference elicitation Layout synthesis Furniture arrangement Space partitioningNotes
Acknowledgments
This work has received funding from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (grant agreement No. [694980] SYNTH: Synthesising Inductive Data Models).
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