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

In this paper, we show that non-uniform distributions in swarms of agents have an impact on the scalability of collective decision-making. In particular, we highlight the relevance of noise-induced bistability in very sparse swarm systems and the failure of these systems to scale. Our work is based on three decision models. In the first model, each agent can change its decision after being recruited by a nearby agent. The second model captures the dynamics of dense swarms controlled by the majority rule (i.e., agents switch their opinion to comply with that of the majority of their neighbors). The third model combines the first two, with the aim of studying the role of non-uniform swarm density in the performance of collective decision-making. Based on the three models, we formulate a set of requirements for convergence and scalability in collective decision-making

Decentralized Connectivity-Preserving Deployment of Large-Scale Robot Swarms

We present a decentralized and scalable approach for deployment of a robot swarm. Our approach tackles scenarios in which the swarm must reach multiple spatially distributed targets, and enforce the constraint that the robot network cannot be split. The basic idea behind our work is to construct a logical tree topology over the physical network formed by the robots. The logical tree acts as a backbone used by robots to enforce connectivity constraints. We study and compare two algorithms to form the logical tree: outwards and inwards. These algorithms differ in the order in which the robots join the tree: the outwards algorithm starts at the tree root and grows towards the targets, while the inwards algorithm proceeds in the opposite manner. Both algorithms perform periodic reconfiguration, to prevent suboptimal topologies from halting the growth of the tree. Our contributions are (i) The formulation of the two algorithms; (ii) A comparison of the algorithms in extensive physics-based simulations; (iii) A validation of our findings through real-robot experiments.Comment: 8 pages, 8 figures, submitted to IROS 201

An HCI quality attributes taxonomy for an impact analysis to interactive systems design and improvement

In the interaction between users and systems, software quality attributes are mainly involved. When designing interfaces for human-computer interaction different alternatives can be considered in order to obtain the highest quality in an interactive system. However, quality attributes have positive and negative contribution relationships among each other, so that a change in one of them can cause a higher improvement than expected or an unwanted degradation of the system. This is the reason why in this paper we propose a taxonomy of non-functional requirements that can be assigned quality properties susceptible to be measured to propose alternatives that achieve a better quality for the systems. Quality that can be obtained by taking into account the contribution relationships among quality attributes, in order to select those alternatives that provide the biggest gain of system quality for the design and improvement of systems and software interfaces.XIII Workshop Ingeniería de Software (WIS).Red de Universidades con Carreras en Informática (RedUNCI