We present AutoOptimization, a novel multi-objective optimization framework for adapting user interfaces. From a user's verbal preferences for changing a UI, our framework guides a prioritization-based Pareto frontier search over candidate layouts. It selects suitable objective functions for UI placement while simultaneously parameterizing them according to the user's instructions to define the optimization problem. A solver then generates a series of optimal UI layouts, which our framework validates against the user's instructions to adapt the UI with the final solution. Our approach thus overcomes the previous need for manual inspection of layouts and the use of population averages for objective parameters. We integrate multiple agents sequentially within our framework, enabling the system to leverage their reasoning capabilities to interpret user preferences, configure the optimization problem, and validate optimization outcomes.

翻译：本文提出AutoOptimization，一种用于调整用户界面的新型多目标优化框架。基于用户对界面更改的语言偏好，本框架引导基于优先级的帕累托前沿搜索遍历候选布局。该框架为界面布局选择合适的目标函数，同时根据用户指令对其进行参数化以定义优化问题。随后，求解器生成一系列最优界面布局，本框架根据用户指令验证这些布局，并采用最终方案调整界面。该方法克服了以往需要人工检查布局及使用群体平均值设定目标参数的局限。我们在框架中顺序集成多个智能体，使系统能够利用其推理能力来解读用户偏好、配置优化问题并验证优化结果。