mlsanityData to 14 Oct 2026

Design Strategy Network: A deep hierarchical framework to represent generative design strategies in complex action spaces

arXiv 2110.03760 ↗First posted 7 Oct 2021cs.AIcited by 4

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Abstract (arXiv)

Generative design problems often encompass complex action spaces that may be divergent over time, contain state-dependent constraints, or involve hybrid (discrete and continuous) domains. To address those challenges, this work introduces Design Strategy Network (DSN), a data-driven deep hierarchical framework that can learn strategies over these arbitrary complex action spaces. The hierarchical architecture decomposes every action decision into first predicting a preferred spatial region in the design space and then outputting a probability distribution over a set of possible actions from that region. This framework comprises a convolutional encoder to work with image-based design state representations, a multi-layer perceptron to predict a spatial region, and a weight-sharing network to generate a probability distribution over unordered set-based inputs of feasible actions. Applied to a truss design study, the framework learns to predict the actions of human designers in the study, capturing their truss generation strategies in the process. Results show that DSNs significantly outperform non-hierarchical methods of policy representation, demonstrating their superiority in complex action space problems.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 30 of its 73 references that are in the map. First posted on arXiv 7 Oct 2021.

Builds on

30 of 73 references.

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Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 1.

4 papers.

  1. 2023Conceptual Design Generation Using Large Language Modelscited by 13
  2. 2024Generative AI and Process Systems Engineering: The Next Frontiercited by 9
  3. 2023Opportunities for Large Language Models and Discourse in Engineering Designcited by 6
  4. 2022Learning to design without prior data: Discovering generalizable design strategies using deep learning and tree searchcited by 5