How can agents best collaborate on sequenced tasks and paths?
CTS-CBS: A New Approach for Multi-Agent Collaborative Task Sequencing and Path Finding
This paper introduces CTS-MAPF, a multi-agent pathfinding problem where agents must complete a series of tasks in order before reaching their destinations, like a multi-stop delivery route for multiple robots. The proposed solution, CTS-CBS, combines task sequencing (like figuring out the best order of deliveries) with collision-free path planning (making sure the robots don't crash). Key for LLM-based multi-agent systems: the hierarchical approach of CTS-CBS could be adapted to manage complex multi-agent interactions, with a high level handling task delegation and sequencing (potentially leveraging LLMs for decision-making) and a low level focusing on individual agent actions, ensuring coherence and efficiency. The concept of K-best solutions for task sequencing is also relevant, allowing exploration of alternative strategies, potentially enhancing the robustness of LLM-driven multi-agent systems.