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/.

翻译：我们提出面向设计的多目标反事实方法（MCD），这是一种针对设计问题的新型反事实优化方法。反事实是可能导向不同决策或选择的情景假设。本文将反事实搜索问题框架化为设计推荐工具，能够识别设计方案的修改路径，从而提升功能性能。MCD通过支持多目标查询（这对设计问题至关重要）以及解耦反事实搜索与采样过程，改进了现有反事实搜索方法，从而提升效率并促进目标权衡的可视化。论文首先通过二维测试案例展示MCD的核心功能，随后以三个自行车设计案例研究验证其在真实设计问题中的有效性。第一案例中，MCD能高效推荐修改查询设计方案，显著提升功能性能（如减重与结构安全系数改进）。第二案例证明MCD可配合预训练语言模型，基于主观文本提示有效提出设计变更建议。最后，论文赋予MCD同时增强查询设计与目标图像及文本提示的相似性、降低重量并优化结构性能的复合任务，展示了其在复杂多模态查询中的表现。总体而言，MCD通过探索假设性设计修改及其对多个设计目标的影响，能够为设计实践者与自动化设计研究人员提供宝贵建议，回应其"如果..."类探索需求。本文使用的代码、测试问题及数据集已在decode.mit.edu/projects/counterfactuals/公开。