# Learning to design from humans: Imitating human designers through deep learning

arXiv: https://arxiv.org/abs/1907.11813 · HTML: https://mlsanity.com/paper/1907.11813/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2019.

## Built on by (most cited first, 8 of 8)

- [Deep Generative Models in Engineering Design: A Review](https://mlsanity.com/paper/2110.10863/) (2021)
- [Data efficient surrogate modeling for engineering design: Ensemble-free batch mode deep active learning for regression](https://mlsanity.com/paper/2211.10360/) (2022)
- [Toward AI Assistants That Let Designers Design](https://mlsanity.com/paper/2107.13074/) (2021)
- [Learning to design without prior data: Discovering generalizable design strategies using deep learning and tree search](https://mlsanity.com/paper/2211.15068/) (2022)
- [Design Strategy Network: A deep hierarchical framework to represent generative design strategies in complex action spaces](https://mlsanity.com/paper/2110.03760/) (2021)
- [Supervising Ralph Wiggum: Exploring a Metacognitive Co-Regulation Agentic AI Loop for Engineering Design](https://mlsanity.com/paper/2603.24768/) (2026)
- [Goal-Directed Design Agents: Integrating Visual Imitation with One-Step Lookahead Optimization for Generative Design](https://mlsanity.com/paper/2110.03223/) (2021)
- [A Meta-Generation framework for Industrial System Generation](https://mlsanity.com/paper/2306.05123/) (2023)

## Abstract (arXiv)

Humans as designers have quite versatile problem-solving strategies. Computer agents on the other hand can access large scale computational resources to solve certain design problems. Hence, if agents can learn from human behavior, a synergetic human-agent problem solving team can be created. This paper presents an approach to extract human design strategies and implicit rules, purely from historical human data, and use that for design generation. A two-step framework that learns to imitate human design strategies from observation is proposed and implemented. This framework makes use of deep learning constructs to learn to generate designs without any explicit information about objective and performance metrics. The framework is designed to interact with the problem through a visual interface as humans did when solving the problem. It is trained to imitate a set of human designers by observing their design state sequences without inducing problem-specific modelling bias or extra information about the problem. Furthermore, an end-to-end agent is developed that uses this deep learning framework as its core in conjunction with image processing to map pixel-to-design moves as a mechanism to generate designs. Finally, the designs generated by a computational team of these agents are then compared to actual human data for teams solving a truss design problem. Results demonstrates that these agents are able to create feasible and efficient truss designs without guidance, showing that this methodology allows agents to learn effective design strategies.