設計的反事實：一種與模型無關的設計推薦方法。

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

我們介紹了一種名為多目標反事實（Multi-Objective Counterfactuals for Design，簡稱MCD）的新方法，用於設計問題中的反事實優化。反事實是可能導致不同決策或選擇的假設情況。在本文中，作者將反事實搜索問題定義為一種設計建議工具，可以幫助識別對設計進行修改，從而提高功能性能。MCD通過支持多目標查詢（在設計問題中至關重要）以及解耦反事實搜索和抽樣過程來改進現有的反事實搜索方法，從而提高效率並促進客觀權衡可視化。本文使用二維測試案例展示了MCD的核心功能，隨後進行了三個自行車設計案例研究，展示了MCD在實際設計問題中的有效性。在第一個案例研究中，MCD擅長於推薦對查詢設計進行修改，從而顯著提高功能性能，例如節省重量和改善結構安全系數。第二個案例研究展示了MCD可以與預先訓練的語言模型合作，有效地根據主觀文本提示提出設計更改。最後，作者讓MCD增加查詢設計與目標圖像和文本提示的相似性，同時減輕重量並提高結構性能，展示了MCD在複雜多模態查詢上的性能。總的來說，MCD有潛力為尋找答案的實踐者和設計自動化研究人員提供有價值的建議，他們通過探索假設設計修改及其對多個設計目標的影響來回答他們的“假如”問題。本文使用的代碼、測試問題和數據集可向公眾提供，網址為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/.