New Variations of the Online k-Canadian Traveler Problem: Uncertain Costs at Known Locations

Davood Shiri, Sibel Salman
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引用次数: 1

Abstract

In this chapter, we study new variations of the online k -Canadian Traveler Problem ( k -CTP) in which there is an input graph with a given source node O and a destination node D. For a specified set consisting of k edges, the edge costs are unknown (we call these uncertain edges). Costs of the remaining edges are known and given. The objective is to find an online strategy such that the traveling agent finds a route from O to D with minimum total travel cost. The agent learns the cost of an uncertain edge, when she arrives at one of its end-nodes and decides on her travel path based on the discovered cost. We call this problem the online k -Cana-dian Traveler Problem with uncertain edges. We analyze both the single-agent and the multi-agent versions of the problem. We propose a tight lower bound on the competitive ratio of deterministic online strategies together with an optimal online strategy for the single-agent version. We consider the multi-agent version with two different objectives. We suggest lower bounds on the competitive ratio of deterministic online strategies to these two problems.
在线k-加拿大旅行者问题的新变体:已知地点的不确定成本
在本章中,我们研究了在线k -加拿大旅行者问题(k -CTP)的新变体,其中有一个输入图,具有给定的源节点O和目标节点d。对于由k条边组成的指定集合,边的成本是未知的(我们称之为不确定边)。剩余边的代价是已知和给定的。目标是找到一个在线策略,使旅行社找到一条从O到D的总旅行成本最小的路线。当智能体到达不确定边缘的一个端点节点时,它学习到不确定边缘的成本,并根据发现的成本决定自己的旅行路径。我们称这个问题为带不确定边的在线k -加拿大旅行者问题。我们分析了该问题的单智能体和多智能体版本。我们提出了确定性在线策略竞争率的严格下界,以及单智能体版本的最优在线策略。我们考虑具有两个不同目标的多智能体版本。针对这两个问题,我们提出了确定性在线策略竞争比的下界。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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