A Design Pattern for Decentralised Decision Making

Abstract

The engineering of large-scale decentralised systems requires sound methodologies to guarantee the attainment of the desired macroscopic system-level behaviour given the microscopic individual-level implementation. While a general-purpose methodology is currently out of reach, specific solutions can be given to broad classes of problems by means of well-conceived design patterns. We propose a design pattern for collective decision making grounded on experimental/theoretical studies of the nest-site selection behaviour observed in honeybee swarms (Apis mellifera). The way in which honeybee swarms arrive at consensus is fairly well-understood at the macroscopic level. We provide formal guidelines for the microscopic implementation of collective decisions to quantitatively match the macroscopic predictions. We discuss implementation strategies based on both homogeneous and heterogeneous multiagent systems, and we provide means to deal with spatial and topological factors that have a bearing on the micro-macro link. Finally, we exploit the design pattern in two case studies that showcase the viability of the approach. Besides engineering, such a design pattern can prove useful for a deeper understanding of decision making in natural systems thanks to the inclusion of individual heterogeneities and spatial factors, which are often disregarded in theoretical modelling.

The Best-of-n Problem in Robot Swarms: Formalization, State of the Art, and Novel Perspectives

“…Recently, Reina et al (2015b) studied a binary foraging scenario, where the objective of the swarm is to decide which foraging patch offers the highest quality resource and to forage from that patch. The environment is characterized by a central retrieval area and two foraging patches.…”

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

“…It has previously been suggested that swarms should be understood as informationprocessing cognitive systems Reina et al 2015b). In contrast to traditional information-based approaches, the ICR framework characterises information flow not in terms of transfer entropy, but in terms of the information gain rate, i.…”

“…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).…”

The impact of agent density on scalability in collective systems: noise-induced versus majority-based bistability

“…The negative feedback by stop signals increases the reliability of the decision-making process because it solves the problem of deadlocks and triggers bistable distributions (cf. [31,32]). In [23] the authors investigated the role of positive/negative feedbacks in addition to noise in the emergence of self organization and collective decision-making.…”