Andreagiovanni Reina

A Design Pattern for Decentralised Decision Making.

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.

A quantitative micro–macro link for collective decisions: the shortest path discovery/selection example

In this paper, we study how to obtain a quantitative correspondence between the dynamics of the microscopic implementation of a robot swarm and the dynamics of a macroscopic model of nest-site selection in honeybees. We do so by considering a collective decision-making case study: the shortest path discovery/selection problem. In this case study, obtaining a quantitative correspondence between the microscopic and macroscopic dynamics—the so-called micro–macro link problem—is particularly challenging because the macroscopic model does not take into account the spatial factors inherent to the path discovery/selection problem. We frame this study in the context of a general engineering methodology that prescribes the inclusion of available theoretical knowledge about target macroscopic models into design patterns for the microscopic implementation. The attainment of the micro–macro link presented in this paper represents a necessary step towards the formalisation of a design pattern for collective decision making in distributed systems.

Emergence of Consensus in a Multi-Robot Network: from Abstract Models to Empirical Validation

Consensus dynamics in decentralised multiagent systems are subject to intense studies, and several different models have been proposed and analyzed. Among these, the naming game stands out for its simplicity and applicability to a wide range of phenomena and applications, from semiotics to engineering. Despite the wide range of studies available, the implementation of theoretical models in real distributed systems is not always straightforward, as the physical platform imposes several constraints that may have a bearing on the consensus dynamics. In this letter, we investigate the effects of an implementation of the naming game for the kilobot robotic platform, in which we consider concurrent execution of games and physical interferences. Consensus dynamics are analyzed in the light of the continuously evolving communication network created by the robots, highlighting how the different regimes crucially depend on the robot density and on their ability to spread widely in the experimental arena. We find that physical interferences reduce the benefits resulting from robot mobility in terms of consensus time, but also result in lower cognitive load for individual agents.

An Experiment in Automatic Design of Robot Swarms

We present an experiment in automatic design of robot swarms. For the first time in the swarm robotics literature, we perform an objective comparison of multiple design methods: we compare swarms designed by two automatic methods—vanilla and EvoStick—with swarms manually designed by human experts. vanilla and EvoStick have been previously published and tested on two tasks. To evaluate their generality, in this paper we test them without any modification on five new tasks. Besides confirming that vanilla is effective, our results provide new insight into the design of robot swarms. In particular, our results indicate that, at least under the adopted experimental protocol, not only does automatic design suffer from the reality gap, but also manual design. The results also show that both manual and automatic methods benefit from bias injection. In this work, bias injection consists in restricting the design search space to the combinations of pre-existing modules. The results indicate that bias injection helps to overcome the reality gap, yielding better performing robot swarms.

Model of the best-of-N nest-site selection process in honeybees

The ability of a honeybee swarm to select the best nest site plays a fundamental role in determining the future colonys fitness. To date, the nest-site selection process has mostly been modeled and theoretically analyzed for the case of binary decisions. However, when the number of alternative nests is larger than two, the decision-process dynamics qualitatively change. In this work, we extend previous analyses of a value-sensitive decision-making mechanism to a decision process among N nests. First, we present the decision-making dynamics in the symmetric case of N equal-quality nests. Then, we generalize our findings to a best-of-N decision scenario with one superior nest and N-1 inferior nests, previously studied empirically in bees and ants. Whereas previous binary models highlighted the crucial role of inhibitory stop-signaling, the key parameter in our new analysis is the relative time invested by swarm members in individual discovery and in signaling behaviors. Our new analysis reveals conflicting pressures on this ratio in symmetric and best-of-N decisions, which could be solved through a time-dependent signaling strategy. Additionally, our analysis suggests how ecological factors determining the density of suitable nest sites may have led to selective pressures for an optimal stable signaling ratio.

Towards a Cognitive Design Pattern for Collective Decision-Making

We introduce the concept of cognitive design pattern to provide a design methodology for distributed multi-agent systems. A cognitive design pattern is a reusable solution to tackle problems requiring cognitive abilities (e.g., decision-making, attention, categorisation). It provides theoretical models and design guidelines to define the individual control rules in order to obtain a desired behaviour for the multiagent system as a whole. In this paper, we propose a cognitive design pattern for collective decision-making inspired by the nest-site selection behaviour of honeybee swarms. We illustrate how to apply the pattern to a case study involving spatial factors: the collective selection of the shortest path between two target areas. We analyse the dynamics of the multi-agent system and we show a very good agreement with the predictions of the macroscopic model.

ARK: Augmented Reality for Kilobots

Working with large swarms of robots has challenges in calibration, sensing, tracking, and control due to the associated scalability and time requirements. Kilobots solve this through their ease of maintenance and programming, and are widely used in several research laboratories worldwide where their low cost enables large-scale swarms studies. However, the small, inexpensive nature of the Kilobots limits their range of capabilities as they are only equipped with a single sensor. In some studies, this limitation can be a source of motivation and inspiration, while in others it is an impediment. As such, we designed, implemented, and tested a novel system to communicate personalized location-and-state-based information to each robot, and receive information on each robots’ state. In this way, the Kilobots can sense additional information from a virtual environment in real time; for example, a value on a gradient, a direction toward a reference point or a pheromone trail. The augmented reality for Kilobots ( ARK) system implements this in flexible base control software which allows users to define varying virtual environments within a single experiment using integrated overhead tracking and control. We showcase the different functionalities of the system through three demos involving hundreds of Kilobots. The ARK provides Kilobots with additional and unique capabilities through an open-source tool which can be implemented with inexpensive, off-the-shelf hardware.

Augmented reality for robots: Virtual sensing technology applied to a swarm of e-pucks

We present a novel technology that allows real robots to perceive an augmented reality environment through virtual sensors. Virtual sensors are a useful and desirable technology for research activities because they allow researchers to quickly and efficiently perform experiments that would otherwise be more expensive, or even impossible. In particular, augmented reality is useful (i) for prototyping and assessing the impact of new sensors before they are physically produced; and (ii) for developing and studying the behaviour of robots that should deal with phenomena that cannot be easily reproduced in a laboratory environment because, for example, they are dangerous (e.g., fire, radiations). We realised an augmented reality system for robots in which a simulator retrieves real-time data on the real environment through a multi-camera tracking system and delivers post-processed information to the robot swarm according to each robots sensing range. We illustrate the proposed virtual sensing technology through an experiment involving 15 e-pucks.

Effects of Spatiality on Value-Sensitive Decisions Made by Robot Swarms

Value-sensitive decision-making is an essential task for organisms at all levels of biological complexity and consists of choosing options among a set of alternatives and being rewarded according to the quality value of the chosen option. Provided that the chosen option has an above-threshold quality value, value-sensitive decisions are particularly relevant in case not all of the possible options are available at decision time. This means that the decision-maker may refrain from deciding until a sufficient-quality option becomes available. Value-sensitive collective decisions are interesting for swarm robotics when the options are dispersed in space (e.g., resources in a foraging problem), and may be discovered at different times. However, current design methodologies for collective decision-making often assume a well-mixed system, and clever design workarounds are suggested to deal with a heterogeneous distribution of opinions within the swarm (e.g., due to spatial constraints on the interaction network). Here, we quantify the effects of spatiality in a value-sensitive decision problem involving a swarm of 150 kilobots. We present a macroscopic model of value-sensitive decision-making inspired by house-hunting honeybees, and implement a solution for both a multiagent system and a kilobot swarm. Notably, no workaround is implemented to deal with the spatial distribution of opinions within the swarm. We show how the dynamics presented by the robotic system match or depart from the model predictions in both a qualitative and quantitative way as a result of spatial constraints.

Collective decision-making

Collective decision-making is the subfield of collective behaviour concerned with how groups reach decisions. Almost all aspects of behaviour can be considered in a decision-making context, but here we focus primarily on how groups should optimally reach consensus, what criteria decision-makers should optimise, and how individuals and groups should forage to optimise their nutrition. We argue for deep parallels between understanding decisions made by individuals and by groups, such as the decision-guiding principle of value-sensitivity. We also review relevant theory and empirical development for the study of collective decision making, including the use of robots.