多智能体路径搜索的基于简化的求解器：调查、讨论和未来的机会
Compilation-based Solvers for Multi-Agent Path Finding: a Survey, Discussion, and Future Opportunities
摘要：
多智能体路径寻找（MAPF）在人工智能领域以及机器人领域和仓储物流等领域都受到了广泛的关注。标准MAPF中的任务是找到路径，通过该路径，智能体可以从其起始位置导航到指定的各个目标位置。两个额外要求的组合使得问题在计算上具有挑战性：（i）代理必须不相互碰撞，以及（ii）路径必须相对于某些目标是最优的。最佳MAPF求解的两种主要方法包括（1）直接求解MAPF的专用基于搜索的方法，以及（2）将MAPF实例简化为不同的良好建立的形式主义中的实例的基于编译的方法，对于该方法存在有效的求解器。基于编译的MAPF求解可以受益于目标求解器开发过程中积累的进步，通常长达数十年。我们总结和比较当代基于编译的求解器MAPF使用ASP，MIP和SAT等形式主义。我们展示了从过去的发展和当前的趋势中吸取的教训，并讨论了其更广泛的影响。
英文摘要：
Multi-agent path finding (MAPF) attracts considerable attention in artificial intelligence community as well as in robotics, and other fields such as warehouse logistics. The task in the standard MAPF is to find paths through which agents can navigate from their starting positions to specified individual goal positions. The combination of two additional requirements makes the problem computationally challenging: (i) agents must not collide with each other and (ii) the paths must be optimal with respect to some objective. Two major approaches to optimal MAPF solving include (1) dedicated search-based methods, which solve MAPF directly, and (2) compilation-based methods that reduce a MAPF instance to an instance in a different well established formalism, for which an efficient solver exists. The compilation-based MAPF solving can benefit from advancements accumulated during the development of the target solver often decades long. We summarize and compare contemporary compilation-based solvers for MAPF using formalisms like ASP, MIP, and SAT. We show the lessons learned from past developments and current trends in the topic and discuss its wider impact.
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