How to allocate tasks in unknown, dynamic environments?
Swarm Algorithms for Dynamic Task Allocation in Unknown Environments*
September 17, 2024
https://arxiv.org/pdf/2409.09550This paper compares different algorithms for coordinating a swarm of robots to complete tasks that appear dynamically in an unknown environment. One algorithm (PROP) uses communication to guide agents to tasks, while another uses a random walk approach. Results show PROP is more efficient when tasks appear slowly, but less so when tasks appear quickly. The authors introduce hybrid approaches that combine elements of both algorithms, achieving greater efficiency in various task-rate scenarios.
Key points for LLM-based multi-agent systems:
- Task allocation strategies: PROP, reminiscent of message-passing in LLMs, excels in environments with sparse tasks. Balancing communication with exploration (like random walks) is crucial for adaptability.
- Dynamic task handling: The paper simulates tasks appearing and disappearing, which is directly relevant to LLM agents handling a stream of requests or events in a changing environment.
- Agent specialization: The use of "propagator" agents to relay information highlights the potential benefits of specialized LLM agents for tasks like task discovery or coordination.