环图与蝌蚪图上的多智能体在线图探索
Multi-Agent Online Graph Exploration on Cycles and Tadpole Graphs
AI总结：
研究多智能体在线图探索问题，针对2-4个智能体在环图和蝌蚪图上的探索，在时间模型和能量模型下分别证明了竞争比的最优结果及上界。
摘要：
我们研究多智能体在线图探索问题，其中由k个智能体组成的团队必须探索给定的图，从同一节点开始并在同一节点结束。该图最初是未知的。当智能体访问某个节点时，其邻域和相邻边会被揭示。智能体共享图已探索部分的全局视图。探索成本必须最小化，其中成本要么描述整个探索所需的时间（时间模型），要么描述任何智能体遍历的最长路径长度（能量模型）。我们研究了2-4个智能体在环图和蝌蚪图上的图探索，在能量模型中提供了竞争比的最优结果（环图上两个智能体和蝌蚪图上三个智能体时为1-competitive），并在时间模型中针对蝌蚪图提供了最优结果（四个智能体时为1.5-competitive）。我们还展示了在时间模型中，三个智能体探索蝌蚪图的竞争上界为2，两个智能体探索蝌蚪图的竞争上界为2.5。
英文摘要：
We study the problem of multi-agent online graph exploration, in which a team of k agents has to explore a given graph, starting and ending on the same node. The graph is initially unknown. Whenever a node is visited by an agent, its neighborhood and adjacent edges are revealed. The agents share a global view of the explored parts of the graph. The cost of the exploration has to be minimized, where cost either describes the time needed for the entire exploration (time model), or the length of the longest path traversed by any agent (energy model). We investigate graph exploration on cycles and tadpole graphs for 2-4 agents, providing optimal results on the competitive ratio in the energy model (1-competitive with two agents on cycles and three agents on tadpole graphs), and for tadpole graphs in the time model (1.5-competitive with four agents). We also show competitive upper bounds of 2 for the exploration of tadpole graphs with three agents, and 2.5 for the exploration of tadpole graphs with two agents in the time model.
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。
