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群或AI智能体的强大工具。然而，这些系统所做出的决策往往依赖于领域专家设计的组件，这使得非技术最终用户难以理解或根据自身需求进行调整。本文提出了一种面向任务分配系统的灵活自然语言接口的初步设计方案。该方法的目标既在于赋予用户对任务分配系统决策过程更大的控制权，也旨在提升这些决策的透明度。用户可通过自然语言指令引导任务分配系统，这些指令借助大语言模型（LLM）被转化为混合整数线性规划（MILP）的约束条件。此外，所提出的系统能够向用户提示指令中可能存在的问题，并通过纠正性对话引导用户寻找可行方案。最后，我们描述了在模拟环境Overcooked中的用户评估计划，并阐述了开发灵活、透明任务分配系统的后续步骤。