Locating a Target with an Agent Guided by Unreliable Local Advice
HANUSSE, Nicolas
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Algorithmics for computationally intensive applications over wide scale distributed platforms [CEPAGE]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Algorithmics for computationally intensive applications over wide scale distributed platforms [CEPAGE]
ILCINKAS, David
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Algorithmics for computationally intensive applications over wide scale distributed platforms [CEPAGE]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Algorithmics for computationally intensive applications over wide scale distributed platforms [CEPAGE]
KOSOWSKI, Adrian
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Algorithmics for computationally intensive applications over wide scale distributed platforms [CEPAGE]
Department of Algorithms and Systems Modelling [ETI GUT] [Gdansk University of Technology]
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Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Algorithmics for computationally intensive applications over wide scale distributed platforms [CEPAGE]
Department of Algorithms and Systems Modelling [ETI GUT] [Gdansk University of Technology]
HANUSSE, Nicolas
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Algorithmics for computationally intensive applications over wide scale distributed platforms [CEPAGE]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Algorithmics for computationally intensive applications over wide scale distributed platforms [CEPAGE]
ILCINKAS, David
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Algorithmics for computationally intensive applications over wide scale distributed platforms [CEPAGE]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Algorithmics for computationally intensive applications over wide scale distributed platforms [CEPAGE]
KOSOWSKI, Adrian
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Algorithmics for computationally intensive applications over wide scale distributed platforms [CEPAGE]
Department of Algorithms and Systems Modelling [ETI GUT] [Gdansk University of Technology]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Algorithmics for computationally intensive applications over wide scale distributed platforms [CEPAGE]
Department of Algorithms and Systems Modelling [ETI GUT] [Gdansk University of Technology]
NISSE, Nicolas
Algorithms, simulation, combinatorics and optimization for telecommunications [MASCOTTE]
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Algorithms, simulation, combinatorics and optimization for telecommunications [MASCOTTE]
Langue
en
Communication dans un congrès
Ce document a été publié dans
Proceedings of the 29th Annual ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing, Proceedings of the 29th Annual ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing, PODC 2010, 2010-07, Zurich. 2010p. 355-364
ACM New York, NY, USA
Résumé en anglais
We study the problem of finding a destination node t by a mobile agent in an unreliable network having the structure of an unweighted graph, in a model first proposed by Hanusse et al [20, 21]. Each node of the network is ...Lire la suite >
We study the problem of finding a destination node t by a mobile agent in an unreliable network having the structure of an unweighted graph, in a model first proposed by Hanusse et al [20, 21]. Each node of the network is able to give advice concerning the next node to visit so as to go closer to the target t. Unfortunately, exactly k of the nodes, called liars, give advice which is incorrect. It is known that for an n-node graph G of maximum degree Δ ≥ 3, reaching a target at a distance of d from the initial location may require an expected time of 2^Ω(min{d,k}), for any d,k = O(log n), even when G is a tree. This paper focuses on strategies which efficiently solve the search problem in scenarios in which, at each node, the agent may only choose between following the local advice, or randomly selecting an incident edge. The strategy which we put forward, called R/A, makes use of a timer (step counter) to alternate between phases of ignoring advice (R) and following advice (A) for a certain number of steps. No knowledge of parameters n, d, or k is required, and the agent need not know by which edge it entered the node of its current location. The performance of this strategy is studied for two classes of regular graphs with extremal values of expansion, namely, for rings and for random Δ-regular graphs (an important class of expanders). For the ring, R/A is shown to achieve an expected searching time of 2d+k^Θ(1) for a worst-case distribution of liars, which is polynomial in both d and k. For random Δ-regular graphs, the expected searching time of the R/A strategy is O(k^3 log^3 n) a.a.s. The polylogarithmic factor with respect to n cannot be dropped from this bound; in fact, we show that a lower time bound of Ω(log n) steps holds for all d,k = Ω(log logn) in random Ω-regular graphs a.a.s. and applies even to strategies which make use of some knowledge of the environment. Finally, we study oblivious strategies which do not use any memory (in particular, with no timer). Such strategies are essentially a form of a random walk, possibly biased by local advice. We show that such biased random walks sometimes achieve drastically worse performance than the R/A strategy. In particular, on the ring, no biased random walk can have a searching time which is polynomial in d and k.< Réduire
Mots clés en anglais
Distributed Computing
Mobile Agent
Random Walks
Expanders
Faulty Networks
Project ANR
Algorithm Design and Analysis for Implicitly and Incompletely Defined Interaction Networks - ANR-07-BLAN-0322
Origine
Importé de halUnités de recherche