How can a single agent plan effectively in a multi-agent system?
Single-Agent Planning in a Multi-Agent System: A Unified Framework for Type-Based Planners
February 15, 2025
https://arxiv.org/pdf/2502.08950This paper proposes a unified framework for single-agent planning in multi-agent systems where the agent has no prior information about its opponents. It focuses on the trade-off between exploiting known information and exploring to gain more. The framework unifies various planning approaches, from exact solutions (like POMDPs) to approximations (like belief-based MDPs and Monte Carlo Tree Search), allowing for scalability in complex environments.
Key points for LLM-based multi-agent systems:
- Opponent Modeling: The framework uses type-based reasoning, where opponent strategies are represented as types (predefined models). This is relevant to LLMs, which can be used to learn and represent these opponent types.
- Belief Updates: The system maintains a belief over possible opponent types and updates it based on observed actions. LLMs can facilitate more nuanced belief updates by incorporating richer contextual information and reasoning about opponent motivations.
- Planning with Uncertainty: The framework addresses the challenge of planning in partially observable environments with unknown opponent strategies. LLMs can enhance planning by generating diverse hypothetical scenarios and predicting opponent actions.
- Scalability: The proposed framework unifies a range of planners with different levels of complexity, enabling developers to choose an approach that balances performance and computational cost. This is crucial for LLM-based agents, which can be computationally expensive.
- Safe Agents: The paper highlights "safe agents," which prioritize avoiding collisions, as a simple yet effective strategy, particularly in complex scenarios. This concept can be incorporated into LLM-based agents to ensure robust and safe behavior.
- Potential for Hybrid Approaches: The framework suggests using learned models (like those from RL) or Nash Equilibrium solvers as heuristics within the tree search. This opens up possibilities for hybrid approaches combining LLMs with other AI techniques. For instance, an LLM can generate initial strategies, which are then refined using tree search guided by an NE solver.