The best-of-n problem in robot swarms: formalization, state of the art, and novel perspectives

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The best-of-n problem in robot swarms : formalization, state of the art, and novel perspectives. / Valentini, G.; Ferrante, E.; Dorigo, M.

In: Frontiers in Robotics and Artificial Intelligence, Vol. 4, 9, 13.03.2017.

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@article{d6b08b381fcf46b1b94fcd56f0f7dd3d,
title = "The best-of-n problem in robot swarms: formalization, state of the art, and novel perspectives",
abstract = "The ability to collectively choose the best among a finite set of alternatives is a fundamental cognitive skill for robot swarms. In this paper, we propose a formal definition of the best-of-n problem and a taxonomy that details its possible variants. Based on this taxonomy, we analyze the swarm robotics literature focusing on the decision-making problem dealt with by the swarm. We find that, so far, the literature has primarily focused on certain variants of the best-of-n problem, while other variants have been the subject of only a few isolated studies. Additionally, we consider a second taxonomy about the design methodologies used to develop collective decision-making strategies. Based on this second taxonomy, we provide an in-depth survey of the literature that details the strategies proposed so far and discusses the advantages and disadvantages of current design methodologies.",
keywords = "best-of-n problem, collective decision-making, consensus achievement, swarm robotics, self-organization",
author = "G. Valentini and E. Ferrante and M. Dorigo",
year = "2017",
month = mar,
day = "13",
doi = "10.3389/frobt.2017.00009",
language = "English",
volume = "4",
journal = "Frontiers in Robotics and Artificial Intelligence",
issn = "2296-9144",
publisher = "Frontiers",

}

RIS

TY - JOUR

T1 - The best-of-n problem in robot swarms

T2 - formalization, state of the art, and novel perspectives

AU - Valentini, G.

AU - Ferrante, E.

AU - Dorigo, M.

PY - 2017/3/13

Y1 - 2017/3/13

N2 - The ability to collectively choose the best among a finite set of alternatives is a fundamental cognitive skill for robot swarms. In this paper, we propose a formal definition of the best-of-n problem and a taxonomy that details its possible variants. Based on this taxonomy, we analyze the swarm robotics literature focusing on the decision-making problem dealt with by the swarm. We find that, so far, the literature has primarily focused on certain variants of the best-of-n problem, while other variants have been the subject of only a few isolated studies. Additionally, we consider a second taxonomy about the design methodologies used to develop collective decision-making strategies. Based on this second taxonomy, we provide an in-depth survey of the literature that details the strategies proposed so far and discusses the advantages and disadvantages of current design methodologies.

AB - The ability to collectively choose the best among a finite set of alternatives is a fundamental cognitive skill for robot swarms. In this paper, we propose a formal definition of the best-of-n problem and a taxonomy that details its possible variants. Based on this taxonomy, we analyze the swarm robotics literature focusing on the decision-making problem dealt with by the swarm. We find that, so far, the literature has primarily focused on certain variants of the best-of-n problem, while other variants have been the subject of only a few isolated studies. Additionally, we consider a second taxonomy about the design methodologies used to develop collective decision-making strategies. Based on this second taxonomy, we provide an in-depth survey of the literature that details the strategies proposed so far and discusses the advantages and disadvantages of current design methodologies.

KW - best-of-n problem

KW - collective decision-making

KW - consensus achievement

KW - swarm robotics

KW - self-organization

UR - http://bio.kuleuven.be/ento/ferrante/papers/2017_ValentiniFrontiers.pdf

U2 - 10.3389/frobt.2017.00009

DO - 10.3389/frobt.2017.00009

M3 - Article

VL - 4

JO - Frontiers in Robotics and Artificial Intelligence

JF - Frontiers in Robotics and Artificial Intelligence

SN - 2296-9144

M1 - 9

ER -