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首页> 外文期刊>The Journal of Artificial Intelligence Research >Implicitly Coordinated Multi-Agent Path Finding under Destination Uncertainty: Success Guarantees and Computational Complexity
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Implicitly Coordinated Multi-Agent Path Finding under Destination Uncertainty: Success Guarantees and Computational Complexity

机译:目的地不确定性下的隐式协调多Agent路径寻找:成功保证和计算复杂性

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In multi-agent path finding (MAPF), it is usually assumed that planning is performed centrally and that the destinations of the agents are common knowledge. We will drop both assumptions and analyze under which conditions it can be guaranteed that the agents reach their respective destinations using implicitly coordinated plans without communication. Furthermore, we will analyze what the computational costs associated with such a coordination regime are. As it turns out, guarantees can be given assuming that the agents are of a certain type. However, the implied computational costs are quite severe. In the distributed setting, we either have to solve a sequence of NP-complete problems or have to tolerate exponentially longer executions. In the setting with destination uncertainty, bounded plan existence becomes PSPACE-complete. This clearly demonstrates the value of communicating about plans before execution starts.
机译:在多主体路径查找(MAPF)中,通常假定集中进行计划,并且主体的目的地是常识。我们将放弃这两个假设,并分析在什么条件下可以使用隐式协调的计划而无需沟通就确保代理到达各自的目的地。此外,我们将分析与这种协调机制相关的计算成本。事实证明,可以假设代理为某种类型来提供保证。但是,隐含的计算成本非常高。在分布式环境中,我们要么必须解决一系列NP完全问题,要么必须容忍指数级更长的执行时间。在目的地不确定的情况下,有界计划的存在变为PSPACE-complete。这清楚地证明了在执行开始之前就计划进行沟通的价值。

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