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
SN - 2296-9144
VL - 4
JO - Frontiers in Robotics and Artificial Intelligence
JF - Frontiers in Robotics and Artificial Intelligence
M1 - 9
ER -