Majority-rule opinion dynamics with differential latency: a mechanism for self-organized collective decision-making

Abstract

Collective decision-making is a process whereby the members of a group decide on a course of action by consensus. In this paper, we propose a collective decision-making mechanism for robot swarms deployed in scenarios in which robots can choose between two actions that have the same effects but that have different execution times. The proposed mechanism allows a swarm composed of robots with no explicit knowledge about the difference in execution times between the two actions to choose the one with the shorter execution time. We use an opinion formation model that captures important elements of the scenarios in which the proposed mechanism can be used in order to predict the system's behavior. The model predicts that when the two actions have different average execution times, the swarm chooses with high probability the action with the shorter average execution time. We validate the model's predictions through a swarm robotics experiment in which robot teams Electronic supplementary material The online version of this article (306 Swarm Intell (2011) 5:305-327 must choose one of two paths of different length that connect two locations. Thanks to the proposed mechanism, a swarm made of robot teams that do not measure time or distance is able to choose the shorter path.

Analysing Robot Swarm Decision-Making with Bio-PEPA

“…The case study consists of a swarm of robots that have to collectively identify the shortest path between two possible choices. We validate our results against those presented in [21].…”

“…In this paper, we analyse a collective decision-making system originally proposed by Montes de Oca et al [21]. The task of the robots is to transport objects from a start area to a goal area.…”

“…Moreover, Bio-PEPA allows to easily define species, which can be used to characterize groups of robots with specific attributes and actions; for instance, they can be used to differentiate between groups of robots performing different tasks at the same location. We use Bio-PEPA to develop a formal specification and analyse a collective decision-making behaviour which has been extensively studied in [21,22]. The case study consists of a swarm of robots that have to collectively identify the shortest path between two possible choices.…”

The Information-Cost-Reward framework for understanding robot swarm foraging

“…Because of this, it is not possible to use it for precisely predicting or optimising swarm performance, or to study macroscopic robot population dynamics, as can be done by using probabilistic finite state machines and differential equations (e.g. Liu and Winfield 2010;Montes de Oca et al 2011;Mather and Hsieh 2012;Reina et al 2015b;Scheidler et al 2016;Valentini et al 2016). Similarly, it is not possible to use the framework to uncover behaviour rules of agents, as can be done by using Turing Learning (Li et al 2016).…”

“…Liu and Winfield 2010;Reina et al 2015a) and differential equations (DEs) (e.g. Montes de Oca et al 2011;Mather and Hsieh 2012;Reina et al 2015b;Scheidler et al 2016;Valentini et al 2016). A comprehensive overview of these methods can be found in Brambilla et al (2013).…”