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Online Graph Exploration with Advice

  • Stefan Dobrev
  • Rastislav Královič
  • Euripides Markou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7355)

Abstract

We study the problem of exploring an unknown undirected graph with non-negative edge weights. Starting at a distinguished initial vertex s, an agent must visit every vertex of the graph and return to s. Upon visiting a node, the agent learns all incident edges, their weights and endpoints. The goal is to find a tour with minimal cost of traversed edges. This variant of the exploration problem has been introduced by Kalyanasundaram and Pruhs in [18] and is known as a fixed graph scenario. There have been recent advances by Megow, Mehlhorn, and Schweitzer ([19]), however the main question whether there exists a deterministic algorithm with constant competitive ratio (w.r.t. to offline algorithm knowing the graph) working on all graphs and with arbitrary edge weights remains open. In this paper we study this problem in the context of advice complexity, investigating the tradeoff between the amount of advice available to the deterministic agent, and the quality of the solution. We show that Ω(n logn) bits of advice are necessary to achieve a competitive ratio of 1 (w.r.t. an optimal algorithm knowing the graph topology). Furthermore, we give a deterministic algorithm which uses O(n) bits of advice and achieves a constant competitive ratio on any graph with arbitrary weights. Finally, going back to the original problem, we prove a lower bound of 5/2 − ε for deterministic algorithms working with no advice, improving the best previous lower bound of 2 − ε of Miyazaki, Morimoto, and Okabe from [20]. In this case, significantly more elaborate technique was needed to achieve the result.

Keywords

Minimum Span Tree Travel Salesman Problem Travel Salesman Problem Competitive Ratio Deterministic Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Stefan Dobrev
    • 1
  • Rastislav Královič
    • 2
  • Euripides Markou
    • 3
  1. 1.Institute of MathematicsSlovak Academy of SciencesBratislavaSlovakia
  2. 2.Department of Computer ScienceComenius UniversityBratislavaSlovakia
  3. 3.Department of Computer Science & Biomedical InformaticsUniversity of Central GreeceLamiaGreece

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