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Conflict Handling Framework in Generalized Multi-agent Path Finding: Advantages and Shortcomings of Satisfiability Modulo Approach

机译:广义多代理路径发现中的冲突处理框架:可满足的多种方法和缺点

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We address conflict reasoning in generalizations of multi-agent path finding (MAPF). We assume items placed in vertices of an undirected graph with at most one item per vertex. Items can be relocated across edges while various constraints depending on the concrete type of MAPF must be satisfied. We recall a general problem formulation that encompasses known types of item relocation problems such as multi-agent path finding (MAPF) and token swapping (TSWAP). We show how to express new types of relocation problems in the general problem formulation. We thoroughly evaluate a novel solving method for item relocation that combines satisfiability modulo theory (SMT) with conflict-based search (CBS). CBS is interpreted in the SMT framework where we start with the basic model and refine the model with a collision resolution constraint whenever a collision between items occurs. The key difference between the standard CBS and the SMT-based modification of CBS (SMT-CBS) is that the standard CBS branches the search to resolve the collision while SMT-CBS iteratively adds a single disjunctive collision resolution constraint. Our experimental evaluation revealed that although SMT-CBS performs better than CBS in small densely occupied instances of variants of MAPF, it is outperformed on large sparsely occupied environments. The performed analysis shows that individual paths in large environments of relocation instances can be found faster using simple A*-based algorithm than by the SMT solver. On the other hand the SMT solver performs better when many conflicts between items need to be resolved.
机译:我们在多智能经验路径查找(MAPF)的概括中地解决了冲突推理。我们假设在每个顶点的最多一个项目中置于无向图的顶点中的项目。必须满足各种限制的边缘,而根据具体类型的MAPF的各种约束必须满足。我们记得一般的问题制定,包括已知类型的项目重定位问题,例如多代理路径查找(MAPF)和令牌交换(TSWAP)。我们展示了如何在一般问题制定中表达新类型的搬迁问题。我们彻底评价了一种新的求解方法,用于将可满足的模数理论(SMT)与基于冲突的搜索(CBS)结合起来的项目重定位。 CBS在SMT框架中解释为我们从基本模型开始,并在发生项目之间的碰撞时,通过碰撞分辨率约束来优化模型。标准CBS和CBS的SMT修改之间的关键差异是标准CBS分支搜索分配碰撞,而SMT-CBS迭代地添加单个分析冲突分辨率约束。我们的实验评价显示,尽管SMT-CBS在MAPF的变种的小密集占用实例中表现得优于CBS,但它在大型稀疏占用环境中表现优于较大。执行的分析表明,使用简单的A *基于算法可以比SMT求解器更快地找到搬迁实例的大型环境中的各个路径。另一方面,当需要解决项目之间的许多冲突时,SMT求解器更好地执行更好。

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