Task assignment and scheduling algorithms are powerful tools for autonomously coordinating large teams of robotic or AI agents. However, the decisions these system make often rely on components designed by domain experts, which can be difficult for non-technical end-users to understand or modify to their own ends. In this paper we propose a preliminary design for a flexible natural language interface for a task assignment system. The goal of our approach is both to grant users more control over a task assignment system's decision process, as well as render these decisions more transparent. Users can direct the task assignment system via natural language commands, which are applied as constraints to a mixed-integer linear program (MILP) using a large language model (LLM). Additionally, our proposed system can alert users to potential issues with their commands, and engage them in a corrective dialogue in order to find a viable solution. We conclude with a description of our planned user-evaluation in the simulated environment Overcooked and describe next steps towards developing a flexible and transparent task allocation system.

翻译：任务分配与调度算法是自主协调大规模机器人或AI智能体团队的有力工具。然而，这些系统做出的决策往往依赖领域专家设计的组件，非技术终端用户难以理解或根据自身需求加以修改。本文提出了一种用于任务分配系统的灵活自然语言接口的初步设计。我们方法的目标是既赋予用户对任务分配系统决策过程的更多控制权，又使这些决策更加透明。用户可通过自然语言指令引导任务分配系统，这些指令作为约束条件，借助大语言模型（LLM）应用于混合整数线性规划（MILP）。此外，我们提出的系统能向用户提示指令中的潜在问题，并通过纠错对话引导他们找到可行解。最后，我们描述了计划在模拟环境Overcooked中进行的用户评估，并阐述了开发灵活且透明的任务分配系统的后续步骤。