# Toward AI Assistants That Let Designers Design

arXiv: https://arxiv.org/abs/2107.13074 · HTML: https://mlsanity.com/paper/2107.13074/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 62 references that are in the map.
- First posted on arXiv 22 Jul 2021.

## Builds on (most cited first, 13 of 13)

- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Cooperative Inverse Reinforcement Learning](https://mlsanity.com/paper/1606.03137/) (2016)
- [Preferential Bayesian Optimization](https://mlsanity.com/paper/1704.03651/) (2017)
- [Shared Autonomy via Hindsight Optimization](https://mlsanity.com/paper/1503.07619/) (2015)
- [Occam's razor is insufficient to infer the preferences of irrational agents](https://mlsanity.com/paper/1712.05812/) (2017)
- [Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behavior](https://mlsanity.com/paper/1805.08010/) (2018)
- [Sequential Gallery for Interactive Visual Design Optimization](https://mlsanity.com/paper/2005.04107/) (2020)
- [Projective Preferential Bayesian Optimization](https://mlsanity.com/paper/2002.03113/) (2020)
- [Generalized Hidden Parameter MDPs Transferable Model-based RL in a Handful of Trials](https://mlsanity.com/paper/2002.03072/) (2020)
- [Data-Driven Design-by-Analogy: State of the Art and Future Directions](https://mlsanity.com/paper/2106.01592/) (2021)
- [GRIDS: Interactive Layout Design with Integer Programming](https://mlsanity.com/paper/2001.02921/) (2020)
- [Learning to design from humans: Imitating human designers through deep learning](https://mlsanity.com/paper/1907.11813/) (2019)
- [Learning an Optimization Algorithm through Human Design Iterations](https://mlsanity.com/paper/1608.06984/) (2016)

## Built on by (most cited first, 6 of 6)

- [Problem Solving Through Human-AI Preference-Based Cooperation](https://mlsanity.com/paper/2408.07461/) (2024)
- [AI for Science: An Emerging Agenda](https://mlsanity.com/paper/2303.04217/) (2023)
- [MenuCraft: Interactive Menu System Design with Large Language Models](https://mlsanity.com/paper/2303.04496/) (2023)
- [User-Centered Design (IX): A "User Experience 3.0" Paradigm Framework in the Intelligence Era](https://mlsanity.com/paper/2302.06681/) (2023)
- [The Boundaries of Automation: A Theory of Persistent Human Participation](https://mlsanity.com/paper/2607.21547/) (2026)
- [TailorCoPilot: Enabling Agentic Pattern Making with Version-Controlled State Tracking](https://mlsanity.com/paper/2608.25462/) (2026)

## Abstract (arXiv)

AI for supporting designers needs to be rethought. It should aim to cooperate, not automate, by supporting and leveraging the creativity and problem-solving of designers. The challenge for such AI is how to infer designers' goals and then help them without being needlessly disruptive. We present AI-assisted design: a framework for creating such AI, built around generative user models which enable reasoning about designers' goals, reasoning, and capabilities.