How can I build adaptable multi-agent task planners?
Adaptive Human-Robot Collaborative Missions using Hybrid Task Planning
This paper proposes a hybrid approach to create robust, adaptable plans for human-robot teams in complex, uncertain environments. It uses classical planning to generate an initial plan and then uses probabilistic model checking and genetic algorithms to refine the plan, considering uncertainties like task failures and changing requirements. This hybrid approach addresses the "state explosion problem" of probabilistic model checking, making it suitable for larger, more realistic scenarios.
The key points for LLM-based multi-agent systems are the decomposition of planning into deterministic and probabilistic stages, the use of meta-heuristic search to optimize plan robustness, and the ability to adapt plans incrementally at runtime in response to changes. This allows for efficient planning and verification in complex, real-world multi-agent systems where perfect prediction is impossible, mimicking how LLMs can generate and refine plans based on evolving contexts.