多智能体系统中的单智能体规划：基于类型的规划器统一框架
Single-Agent Planning in a Multi-Agent System: A Unified Framework for Type-Based Planners
AI总结：
针对多智能体环境中无对手先验信息的单智能体规划权衡问题，提出统一理论框架涵盖精确到近似解的规划器，实验验证13个规划器（含安全智能体及其增强变体）在多智能体路径规划场景的性能，为多智能体决策提供新见解。
摘要：
我们考虑一个一般问题：智能体处于多智能体环境中，必须在没有任何关于对手的先验信息的情况下为自身规划。在每个时刻，这个关键智能体面临着权衡：是利用当前积累的关于其他智能体的信息，还是进一步探索以改进未来（重新）规划。我们提出一个理论框架，统一一系列规划器，供关键智能体解决这种权衡。该谱一端的规划器旨在寻找精确解，而另一端的规划器在问题规模扩大时产生近似解。除理论分析外，我们还实现了13个规划器，并在名为多智能体路径规划的特定领域进行实验，智能体数量最多达50个，以比较它们在各种场景下的性能。一个有趣的观察来自我们称为安全智能体的一类规划器及其通过融入领域特定知识的增强变体，这是所提通用框架下的简单特例，但在大多数情况下表现足够好。我们的统一框架以及由此产生的规划器为多智能体决策提供了新见解，在机制设计等相关领域具有潜在应用。
英文摘要：
We consider a general problem where an agent is in a multi-agent environment and must plan for herself without any prior information about her opponents. At each moment, this pivotal agent is faced with a trade-off between exploiting her currently accumulated information about the other agents and exploring further to improve future (re-)planning. We propose a theoretic framework that unifies a spectrum of planners for the pivotal agent to address this trade-off. The planner at one end of this spectrum aims to find exact solutions, while those towards the other end yield approximate solutions as the problem scales up. Beyond theoretical analysis, we also implement \textbf{13} planners and conduct experiments in a specific domain called \textit{multi-agent route planning} with the number of agents \textbf{up to~50}, to compare their performaces in various scenarios. One interesting observation comes from a class of planners that we call \textit{safe-agents} and their enhanced variants by incorporating domain-specific knowledge, which is a simple special case under the proposed general framework, but performs sufficiently well in most cases. Our unified framework, as well as those induced planners, provides new insights on multi-agent decision-making, with potential applications to related areas such as mechanism design.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
