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.
Highlights
Collective decision-making refers to the phenomenon whereby a collective of agents makes a choice in a way that, once made, it is no longer attributable to any of the individual agents
The distinction between these two situations has been formalized in the context of swarm robotics by Brambilla et al (2013) and organized in two categories: consensus achievement and task allocation
We introduce the best-of-n problem, i.e., an abstraction capturing the structure and logic of discrete consensus achievement problems that need to be solved in several swarm robotics scenarios
Summary
Collective decision-making refers to the phenomenon whereby a collective of agents makes a choice in a way that, once made, it is no longer attributable to any of the individual agents. A different situation arises in the context of other social insect colonies, where workers are able to collectively allocate themselves to a variety of tasks, such as foraging, brood care, and nest construction, and to change their allocation as a function of the colony needs (Pinter-Wollman et al, 2013; Gordon, 2016; Jandt and Gordon, 2016). The distinction between these two situations has been formalized in the context of swarm robotics by Brambilla et al (2013) and organized in two categories: consensus achievement and task allocation (see Figure 1). The first category encompasses systems where agents aim at making a common decision on a certain matter (see Section 4 and Section 5), whereas the second category includes systems where agents allocate themselves to different tasks, with the objective to maximize
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
