How can LLMs explain multi-robot decisions?
CE-MRS: Contrastive Explanations for Multi-Robot Systems
This paper introduces CE-MRS, a system for generating natural language explanations of multi-robot task allocation, scheduling, and motion planning solutions. CE-MRS uses a contrastive approach, comparing the system's solution to user-provided "foil" solutions to highlight key decision factors and potential errors.
Key for LLM-based systems: CE-MRS demonstrates the value of contrastive explanations, a format well-suited to LLMs' text generation capabilities. The paper focuses on grounding explanations in underlying system data (robot traits, task requirements, etc.), which is crucial for LLM-generated explanations to be meaningful and trustworthy. It also tackles a core challenge in multi-agent LLM apps: making complex, interdependent agent actions understandable to humans.