Collective decision making refers to the phenomenon whereby a collective of agents makes a choice in a way that, once made, the choice is no longer attributable to any of the individual agents. This phenomenon is widespread across natural and artificial systems and is studied in a number of different disciplines including psychology, biology, and physics, to name a few. In the case of robot swarms, collective decision- making systems are distinguished between systems for consensus achievement and systems for task allocation. The first category encompasses systems that aim to establish an agreement among agents on a certain matter. The second category deals with systems that aim to allocate agents, i.e., the available workforce, to a set of tasks with the objective to maximize the performance of the collective. In this chapter, we focus on consensus achievement problems. We define the best-of-n problem and a taxonomy of its possible variants. Using this problem-oriented taxonomy, as well as a second taxonomy based on the design methodology, we review the literature of swarm robotics and provide a complete overview of the current state of the art.
Valentini, G. (2017). Discrete consensus achievement in artificial systems. In Studies in Computational Intelligence (Vol. 706, pp. 9–32). Springer Verlag. https://doi.org/10.1007/978-3-319-53609-5_2