par Valentini, Gabriele 
;Ferrante, Eliseo ;Dorigo, Marco
Référence Frontiers in robotics and AI, 4, 9, page (1-18)
Publication Publié, 2017
;Ferrante, Eliseo ;Dorigo, Marco Référence Frontiers in robotics and AI, 4, 9, page (1-18)
Publication Publié, 2017
Article révisé par les pairs
| Résumé : | 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. |
