设计的反事实：一种面向设计推荐的模型无关方法

Counterfactuals for Design: A Model-Agnostic Method For Design Recommendations

May 18, 2023

作者: Lyle Regenwetter, Yazan Abu Obaideh, Faez Ahmed

cs.AI

摘要

我们介绍了多目标反事实设计（MCD），这是一种在设计问题中进行反事实优化的新方法。反事实是可能导致不同决策或选择的假设情况。在本文中，作者将反事实搜索问题构建为一个设计推荐工具，可以帮助识别对设计进行修改，从而实现更好的功能性能。MCD通过支持多目标查询并解耦反事实搜索和采样过程来改进现有的反事实搜索方法，这在设计问题中至关重要，同时提高效率并促进客观权衡可视化。本文使用二维测试案例展示了MCD的核心功能，随后进行了三个自行车设计案例研究，展示了MCD在实际设计问题中的有效性。在第一个案例研究中，MCD擅长推荐对查询设计进行修改，可以显著提升功能性能，如减轻重量和改善结构安全系数。第二个案例研究表明，MCD可以与预训练语言模型配合，有效地根据主观文本提示建议设计更改。最后，作者要求MCD增加查询设计与目标图像和文本提示的相似性，同时减轻重量并提高结构性能，展示了MCD在复杂多模态查询上的表现。总的来说，MCD有潜力为寻找答案的从业者和设计自动化研究人员提供有价值的建议，通过探索假设设计修改及其对多个设计目标的影响来回答他们的“假如”问题。本文使用的代码、测试问题和数据集可供公众访问，网址为decode.mit.edu/projects/counterfactuals/。

English

We introduce Multi-Objective Counterfactuals for Design (MCD), a novel method for counterfactual optimization in design problems. Counterfactuals are hypothetical situations that can lead to a different decision or choice. In this paper, the authors frame the counterfactual search problem as a design recommendation tool that can help identify modifications to a design, leading to better functional performance. MCD improves upon existing counterfactual search methods by supporting multi-objective queries, which are crucial in design problems, and by decoupling the counterfactual search and sampling processes, thus enhancing efficiency and facilitating objective tradeoff visualization. The paper demonstrates MCD's core functionality using a two-dimensional test case, followed by three case studies of bicycle design that showcase MCD's effectiveness in real-world design problems. In the first case study, MCD excels at recommending modifications to query designs that can significantly enhance functional performance, such as weight savings and improvements to the structural safety factor. The second case study demonstrates that MCD can work with a pre-trained language model to suggest design changes based on a subjective text prompt effectively. Lastly, the authors task MCD with increasing a query design's similarity to a target image and text prompt while simultaneously reducing weight and improving structural performance, demonstrating MCD's performance on a complex multimodal query. Overall, MCD has the potential to provide valuable recommendations for practitioners and design automation researchers looking for answers to their ``What if'' questions by exploring hypothetical design modifications and their impact on multiple design objectives. The code, test problems, and datasets used in the paper are available to the public at decode.mit.edu/projects/counterfactuals/.