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Contextually aware intelligent control agents for heterogeneous swarms Swarm Intell. (IF 2.6) Pub Date : 2024-03-08
Abstract An emerging challenge in swarm shepherding research is to design effective and efficient artificial intelligence algorithms that maintain simplicity in their decision models, whilst increasing the swarm’s abilities to operate in diverse contexts. We propose a methodology to design a context-aware swarm control intelligent agent (shepherd). We first use swarm metrics to recognise the type of
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The effect of uneven and obstructed site layouts in best-of-N Swarm Intell. (IF 2.6) Pub Date : 2024-03-07 Jennifer Leaf, Julie A. Adams
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Predictive search model of flocking for quadcopter swarm in the presence of static and dynamic obstacles Swarm Intell. (IF 2.6) Pub Date : 2024-02-24 Giray Önür, Ali Emre Turgut, Erol Şahin
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Belief space-guided approach to self-adaptive particle swarm optimization Swarm Intell. (IF 2.6) Pub Date : 2024-01-31 Daniel von Eschwege, Andries Engelbrecht
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On the evolution of adaptable and scalable mechanisms for collective decision-making in a swarm of robots Swarm Intell. (IF 2.6) Pub Date : 2024-01-19 Ahmed Almansoori, Muhanad Alkilabi, Elio Tuci
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Emergent communication enhances foraging behavior in evolved swarms controlled by spiking neural networks Swarm Intell. (IF 2.6) Pub Date : 2023-12-14 Cristian Jimenez Romero, Alper Yegenoglu, Aarón Pérez Martín, Sandra Diaz-Pier, Abigail Morrison
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Decomposition and merging cooperative particle swarm optimization with random grouping for large-scale optimization problems Swarm Intell. (IF 2.6) Pub Date : 2023-11-14 Alanna McNulty, Beatrice Ombuki-Berman, Andries Engelbrecht
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Elitist artificial bee colony with dynamic population size for multimodal optimization problems Swarm Intell. (IF 2.6) Pub Date : 2023-11-06 Doğan Aydın, Yunus Özcan, Muhammad Sulaiman, Gürcan Yavuz, Zahid Halim
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On the automatic design of multi-objective particle swarm optimizers: experimentation and analysis Swarm Intell. (IF 2.6) Pub Date : 2023-10-09 Antonio J. Nebro, Manuel López-Ibáñez, José García-Nieto, Carlos A. Coello Coello
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Consensus decision-making in artificial swarms via entropy-based local negotiation and preference updating Swarm Intell. (IF 2.6) Pub Date : 2023-05-15 Chuanqi Zheng, Kiju Lee
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Effect of swarm density on collective tracking performance Swarm Intell. (IF 2.6) Pub Date : 2023-03-21 Hian Lee Kwa, Julien Philippot, Roland Bouffanais
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Multi-agent bandit with agent-dependent expected rewards Swarm Intell. (IF 2.6) Pub Date : 2023-03-18 Fan Jiang, Hui Cheng
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Out-of-the-box parameter control for evolutionary and swarm-based algorithms with distributed reinforcement learning Swarm Intell. (IF 2.6) Pub Date : 2023-01-07 Marcelo Gomes Pereira de Lacerda, Fernando Buarque de Lima Neto, Teresa Bernarda Ludermir, Herbert Kuchen
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Three-dimensional relative localization and synchronized movement with wireless ranging Swarm Intell. (IF 2.6) Pub Date : 2022-12-02 Sven Pfeiffer, Veronica Munaro, Shushuai Li, Alessandro Rizzo, Guido C. H. E. de Croon
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Phase transition of a nonlinear opinion dynamics with noisy interactions Swarm Intell. (IF 2.6) Pub Date : 2022-11-17 Francesco d’Amore, Andrea Clementi, Emanuele Natale
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Blending multiple algorithmic granular components: a recipe for clustering Swarm Intell. (IF 2.6) Pub Date : 2022-11-06 Olayinka Idowu Oduntan, Parimala Thulasiraman
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Wildfire detection in large-scale environments using force-based control for swarms of UAVs Swarm Intell. (IF 2.6) Pub Date : 2022-11-01 Georgios Tzoumas, Lenka Pitonakova, Lucio Salinas, Charles Scales, Thomas Richardson, Sabine Hauert
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Drone flocking optimization using NSGA-II and principal component analysis Swarm Intell. (IF 2.6) Pub Date : 2022-10-26 Jagdish Chand Bansal, Nikhil Sethi, Ogbonnaya Anicho, Atulya Nagar
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Collective gradient perception with a flying robot swarm Swarm Intell. (IF 2.6) Pub Date : 2022-10-26 Tugay Alperen Karagüzel, Ali Emre Turgut, A. E. Eiben, Eliseo Ferrante
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A field-based computing approach to sensing-driven clustering in robot swarms Swarm Intell. (IF 2.6) Pub Date : 2022-09-19 Gianluca Aguzzi, Giorgio Audrito, Roberto Casadei, Ferruccio Damiani, Gianluca Torta, Mirko Viroli
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Noise-resistant and scalable collective preference learning via ranked voting in swarm robotics Swarm Intell. (IF 2.6) Pub Date : 2022-09-05 Qihao Shan, Sanaz Mostaghim
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Sample greedy based task allocation for multiple robot systems Swarm Intell. (IF 2.6) Pub Date : 2022-08-13 Hyo-Sang Shin, Teng Li, Hae-In Lee, Antonios Tsourdos
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Emergent naming conventions in a foraging robot swarm Swarm Intell. (IF 2.6) Pub Date : 2022-07-13 Roman Miletitch, Andreagiovanni Reina, Marco Dorigo, Vito Trianni
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On multi-human multi-robot remote interaction: a study of transparency, inter-human communication, and information loss in remote interaction Swarm Intell. (IF 2.6) Pub Date : 2022-03-11 Jayam Patel, Prajankya Sonar, Carlo Pinciroli
In this paper, we investigate how to design an effective interface for remote multi-human–multi-robot interaction. While significant research exists on interfaces for individual human operators, little research exists for the multi-human case. Yet, this is a critical problem to solve to make complex, large-scale missions achievable in which direct operator involvement is impossible or undesirable,
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Distributed deformable configuration control for multi-robot systems with low-cost platforms Swarm Intell. (IF 2.6) Pub Date : 2022-02-26 Seoung Kyou Lee
This work presents a deformable configuration controller—a fully distributed algorithm that enables a swarm of robots to avoid an obstacle while maintaining network connectivity. We assume a group of robots flocking in an unknown environment, each of which has only incomplete knowledge of the geometry without a map, a shared coordinate, or the use of a centralized control scheme. Instead, the controller
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Multi-guide particle swarm optimisation archive management strategies for dynamic optimisation problems Swarm Intell. (IF 2.6) Pub Date : 2022-02-01 Paweł Joćko, Beatrice M. Ombuki-Berman, Andries P. Engelbrecht
This study presents archive management approaches for dynamic multi-objective optimisation problems (DMOPs) using the multi-guide particle swarm optimisation (MGPSO) algorithm by Scheepers et al. (Swarm Intell, 13(3–4):245–276, 2019, https://doi.org/10.1007/s11721-019-00171-0). The MGPSO is a multi-swarm approach developed for static multi-objective optimisation problems, where each subswarm optimises
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Metaphor-based metaheuristics, a call for action: the elephant in the room Swarm Intell. (IF 2.6) Pub Date : 2021-11-30 Claus Aranha,Christian L. Camacho Villalón,Felipe Campelo,Marco Dorigo,Rubén Ruiz,Marc Sevaux,Kenneth Sörensen,Thomas Stützle
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Causes of variation of darkness in flocks of starlings, a computational model Swarm Intell. (IF 2.6) Pub Date : 2021-11-25 A. Costanzo, H. Hildenbrandt, C. K. Hemelrijk
The coordinated motion of large flocks of starlings is fascinating for both laymen and scientists. During their aerial displays, the darkness of flocks often changes, for instance dark bands propagate through the flock (so-called agitation waves) and small or large parts of the flock darken. The causes of dark bands in agitation waves have recently been shown to depend on changes in orientation of
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Robot swarm democracy: the importance of informed individuals against zealots Swarm Intell. (IF 2.6) Pub Date : 2021-11-23 Masi, Giulia De, Prasetyo, Judhi, Zakir, Raina, Mankovskii, Nikita, Ferrante, Eliseo, Tuci, Elio
In this paper we study a generalized case of best-of-n model, which considers three kind of agents: zealots, individuals who remain stubborn and do not change their opinion; informed agents, individuals that can change their opinion, are able to assess the quality of the different options; and uninformed agents, individuals that can change their opinion but are not able to assess the quality of the
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Resource ephemerality influences effectiveness of altruistic behavior in collective foraging Swarm Intell. (IF 2.6) Pub Date : 2021-11-23 Nauta, Johannes, Khaluf, Yara, Simoens, Pieter
In collective foraging, interactions between conspecifics can be exploited to increase foraging efficiencies. Many collective systems exhibit short interaction ranges, making information about patches rich in resources only locally available. In environments wherein these patches are difficult to locate, collective systems might exhibit altruistic traits that increase average resource intake compared
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ANTS 2020 Special Issue: Editorial Swarm Intell. (IF 2.6) Pub Date : 2021-11-23 Marco Dorigo,Thomas Stützle,Maria J. Blesa,Christian Blum,Heiko Hamann,Mary Katherine Heinrich
This special issue of the Swarm Intelligence journal is dedicated to the publication of extended versions of some of the best papers presented at ANTS 2020, Twelfth International Conference on Swarm Intelligence, which took place in Barcelona, Spain, on October 26–28, 2020. Due to the COVID-19 pandemic, the conference was held online. ANTS is the first and most established conference series dedicated
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A machine education approach to swarm decision-making in best-of-n problems Swarm Intell. (IF 2.6) Pub Date : 2021-11-22 Hussein, Aya, Elsawah, Sondoss, Petraki, Eleni, Abbass, Hussein A.
In swarm decision making, hand-crafting agents’ rules that use local information to achieve desirable swarm-level behaviours is a non-trivial design problem. Instead of relying entirely on swarm experts for designing these local rules, machine learning (ML) algorithms can be utilised for learning some of the local rules by mapping an agent’s perception to an appropriate action. To facilitate this process
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Ant colony optimization for feasible scheduling of step-controlled smart grid generation Swarm Intell. (IF 2.6) Pub Date : 2021-10-19 Bremer, Jörg, Lehnhoff, Sebastian
The electrical energy grid is currently experiencing a paradigm shift in control. In the future, small and decentralized energy resources will have to responsibly perform control tasks like frequency or voltage control. For many use cases, scheduling of energy resources is necessary. In the multi-dimensional discrete case–e.g., for step-controlled devices–this is an NP-hard problem if some sort of
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Reinforcement learning as a rehearsal for swarm foraging Swarm Intell. (IF 2.6) Pub Date : 2021-09-29 Nguyen, Trung, Banerjee, Bikramjit
Foraging in a swarm of robots has been investigated by many researchers, where the prevalent techniques have been hand-designed algorithms with parameters often tuned via machine learning. Our departure point is one such algorithm, where we replace a hand-coded decision procedure with reinforcement learning (RL), resulting in significantly superior performance. We situate our approach within the reinforcement
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Discrete collective estimation in swarm robotics with distributed Bayesian belief sharing Swarm Intell. (IF 2.6) Pub Date : 2021-09-05 Shan, Qihao, Mostaghim, Sanaz
Multi-option collective decision-making is a challenging task in the context of swarm intelligence. In this paper, we extend the problem of collective perception from simple binary decision-making of choosing the color in majority to estimating the most likely fill ratio from a series of discrete fill ratio hypotheses. We have applied direct comparison (DC) and direct modulation of voter-based decisions
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Collective decision-making for dynamic environments with visual occlusions Swarm Intell. (IF 2.6) Pub Date : 2021-08-25 Jiang, Fan, Cheng, Hui, Chen, Guanrong
For decades, both empirical and theoretical models have been proposed to explain the patterns and mechanisms of collective decision-making (CDM). The most-studied CDM scenario is the best-of-n problem in a static environment. However, natural environments are typically dynamic. In dynamic environments, the visual occlusions produced by other members of a large-scale group are also common. Hence, some
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HuGoS: a virtual environment for studying collective human behavior from a swarm intelligence perspective Swarm Intell. (IF 2.6) Pub Date : 2021-08-03 Coucke, Nicolas, Heinrich, Mary Katherine, Cleeremans, Axel, Dorigo, Marco
Swarm intelligence studies self-organized collective behavior resulting from interactions between individuals, typically in animals and artificial agents. Some studies from cognitive science have also demonstrated self-organization mechanisms in humans, often in pairs. Further research into the topic of human swarm intelligence could provide a better understanding of new behaviors and larger human
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Achieving task allocation in swarm intelligence with bi-objective embodied evolution Swarm Intell. (IF 2.6) Pub Date : 2021-07-04 Qihao Shan, Sanaz Mostaghim
In this paper, we seek to achieve task allocation in swarm intelligence using an embodied evolutionary framework, which aims to generate divergent and specialized behaviors among a swarm of agents in an online and self-organized manner. In our considered scenario, specialization is encouraged through a bi-objective composite fitness function for the genomes, which is the weighted sum of a local and
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Quorum sensing without deliberation: biological inspiration for externalizing computation to physical spaces in multi-robot systems Swarm Intell. (IF 2.6) Pub Date : 2021-06-17 Theodore P. Pavlic, Jake Hanson, Gabriele Valentini, Sara Imari Walker, Stephen C. Pratt
Quorum sensing (QS) is ubiquitous in distributed, multi-agent systems in nature—from bacteria to arthropods to primates—and has been proposed as a useful distributed algorithm in engineered systems—from multi-robot systems to Internet server farms. Achieving QS requires groups to collectively integrate information about their numbers and reach consensus on an action contingent upon those numbers. In
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Human-collective visualization transparency Swarm Intell. (IF 2.6) Pub Date : 2021-06-03 Karina A. Roundtree, Jason R. Cody, Jennifer Leaf, H. Onan Demirel, Julie A. Adams
Interest in collective robotic systems has increased rapidly due to the potential benefits that can be offered to operators, such as increased safety and support, who perform challenging tasks in high-risk environments. The limited human-collective transparency research has focused on how the design of the models (i.e., algorithms), visualizations, and control mechanisms influence human-collective
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Collective preference learning in the best-of-n problem Swarm Intell. (IF 2.6) Pub Date : 2021-06-02 Michael Crosscombe, Jonathan Lawry
Decentralised autonomous systems rely on distributed learning to make decisions and to collaborate in pursuit of a shared objective. For example, in swarm robotics the best-of-n problem is a well-known collective decision-making problem in which agents attempt to learn the best option out of n possible alternatives based on local feedback from the environment. This typically involves gathering information
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Collective decision-making in living and artificial systems: editorial Swarm Intell. (IF 2.6) Pub Date : 2021-06-01 Andreagiovanni Reina,Eliseo Ferrante,Gabriele Valentini
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Negative updating applied to the best-of-n problem with noisy qualities Swarm Intell. (IF 2.6) Pub Date : 2021-05-25 Chanelle Lee, Jonathan Lawry, Alan F. T. Winfield
The ability to perform well in the presence of noise is an important consideration when evaluating the effectiveness of a collective decision-making framework. Any system deployed for real-world applications will have to perform well in complex and uncertain environments, and a component of this is the limited reliability and accuracy of evidence sources. In particular, in swarm robotics there is an
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Multi-featured collective perception with Evidence Theory: tackling spatial correlations Swarm Intell. (IF 2.6) Pub Date : 2021-05-22 Palina Bartashevich, Sanaz Mostaghim
Collective perception allows sparsely distributed agents to form a global view on a common spatially distributed problem without any direct access to global knowledge and only based on a combination of locally perceived information. However, the evidence gathered from the environment is often subject to spatial correlations and depends on the movements of the agents. The latter is not always easy to
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Analysis and control of agreement and disagreement opinion cascades Swarm Intell. (IF 2.6) Pub Date : 2021-05-21 Alessio Franci, Anastasia Bizyaeva, Shinkyu Park, Naomi Ehrich Leonard
We introduce and analyze a continuous time and state-space model of opinion cascades on networks of large numbers of agents that form opinions about two or more options. By leveraging our recent results on the emergence of agreement and disagreement states, we introduce novel tools to analyze and control agreement and disagreement opinion cascades. New notions of agreement and disagreement centrality
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CONSOLE: intruder detection using a UAV swarm and security rings Swarm Intell. (IF 2.6) Pub Date : 2021-05-19 Daniel H. Stolfi, Matthias R. Brust, Grégoire Danoy, Pascal Bouvry
This article introduces CONcentric Swarm mObiLity modEl (CONSOLE), a novel mobility model for unmanned aerial vehicles (UAVs) to efficiently achieve surveillance and intruder detection missions. It permits to protect a restricted area from intruders using a concentric circles model where simulated UAVs evolve in these so-called security rings. Having UAVs arranged in rings fosters an early detection
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Enhanced or distorted wisdom of crowds? An agent-based model of opinion formation under social influence Swarm Intell. (IF 2.6) Pub Date : 2021-05-07 Pavlin Mavrodiev, Frank Schweitzer
We propose an agent-based model of collective opinion formation to study the wisdom of crowds under social influence. The opinion of an agent is a continuous positive value, denoting its subjective answer to a factual question. The wisdom of crowds states that the average of all opinions is close to the truth, i.e., the correct answer. But if agents have the chance to adjust their opinion in response
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Nest choice in arboreal ants is an emergent consequence of network creation under spatial constraints Swarm Intell. (IF 2.6) Pub Date : 2021-04-24 Joanna Chang, Scott Powell, Elva J. H. Robinson, Matina C. Donaldson-Matasci
Biological transportation networks must balance competing functional priorities. The self-organizing mechanisms used to generate such networks have inspired scalable algorithms to construct and maintain low-cost and efficient human-designed transport networks. The pheromone-based trail networks of ants have been especially valuable in this regard. Here, we use turtle ants as our focal system: In contrast
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Assessing the robustness of decentralized gathering: a multi-agent approach on micro-biological systems Swarm Intell. (IF 2.6) Pub Date : 2020-11-05 Daniele Proverbio, Luca Gallo, Barbara Passalacqua, Marco Destefanis, Marco Maggiora, Jacopo Pellegrino
Adopting a multi-agent systems paradigm, we developed, tested and exploited a computational testbed that simulates gathering features of the social amoeba Dictyostelium discoideum. It features a tailored design and implementation to manage discrete simulations with autonomous agents on a microscopic scale, thus focusing on their social behavior and mutual interactions. Hence, we could assess the behavioral
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Self-adaptive potential-based stopping criteria for Particle Swarm Optimization with forced moves Swarm Intell. (IF 2.6) Pub Date : 2020-09-10 Bernd Bassimir, Manuel Schmitt, Rolf Wanka
We study the variant of Particle Swarm Optimization that applies random velocities in a dimension instead of the regular velocity update equations as soon as the so-called potential of the swarm falls below a certain small bound in this dimension, arbitrarily set by the user. In this case, the swarm performs a forced move. In this paper, we are interested in how, by counting the forced moves, the swarm
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Spatial segregative behaviors in robotic swarms using differential potentials Swarm Intell. (IF 2.6) Pub Date : 2020-07-02 Vinicius G. Santos, Anderson G. Pires, Reza J. Alitappeh, Paulo A. F. Rezeck, Luciano C. A. Pimenta, Douglas G. Macharet, Luiz Chaimowicz
Segregative behaviors, in which individuals with common characteristics are placed together and set apart from other groups, are commonly found in nature. In swarm robotics, these behaviors can be important in different tasks that require a heterogeneous group of robots to be divided in homogeneous sets according to their physical (sensors, actuators) or logical (algorithms) capabilities. In this paper
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Response probability enhances robustness in decentralized threshold-based robotic swarms Swarm Intell. (IF 2.6) Pub Date : 2020-06-06 Annie S. Wu, R. Paul Wiegand, Ramya Pradhan
In this paper, we investigate how response probability may be used to improve the robustness of reactive, threshold-based robotic swarms. In swarms where agents have differing thresholds, adding a response probability is expected to distribute task experiences among more agents, which can increase the robustness of the swarm. If the lowest threshold agents for a task become unavailable, distributing
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On the robustness of consensus-based behaviors for robot swarms Swarm Intell. (IF 2.6) Pub Date : 2020-05-29 Majda Moussa, Giovanni Beltrame
In swarm robotics, behaviors requiring consensus, meaning having the robots agree on a set of variables, have attracted great attention over the years. Determining the robustness and applicability of these behaviors in harsh communication environments is an open area of research. In this paper, we propose the use of a formal software engineering technique, statistical model checking, to model and assess
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Respecializing swarms by forgetting reinforced thresholds Swarm Intell. (IF 2.6) Pub Date : 2020-03-05 Vera A. Kazakova, Annie S. Wu, Gita R. Sukthankar
Response threshold reinforcement is a powerful model for decentralized task allocation and specialization in multiagent swarms. In dynamic environments, initial task assignments and specializations must be updated over time to meet changing system needs. The very nature of threshold reinforcement-based behavior can, however, hinder respecialization, limiting its usability in real-world applications
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A computational model of task allocation in social insects: ecology and interactions alone can drive specialisation Swarm Intell. (IF 2.6) Pub Date : 2020-02-22 Rui Chen, Bernd Meyer, Julian Garcia
Social insects allocate their workforce in a decentralised fashion, addressing multiple tasks and responding effectively to environmental changes. This process is fundamental to their ecological success, but the mechanisms behind it are not well understood. While most models focus on internal and individual factors, empirical evidence highlights the importance of ecology and social interactions. To
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An analytical study on leader and follower switching in V-shaped Canada Goose flocks for energy management purposes Swarm Intell. (IF 2.6) Pub Date : 2020-01-30 A. Mirzaeinia, F. Heppner, M. Hassanalian
Migrating birds may take advantage of V-shaped flocking to reduce the required energy for their flight. Studies have shown that the birds in different positions in V-shaped flight contend with different drag forces. Lead and follower birds may have to overcome more drag forces than the other birds in a V-shaped flock. Some observations of different kinds of flocking birds repositioning within a flock
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A study on force-based collaboration in swarms Swarm Intell. (IF 2.6) Pub Date : 2019-11-11 Chiara Gabellieri, Marco Tognon, Dario Sanalitro, Lucia Pallottino, Antonio Franchi
Cooperative manipulation is a basic skill in groups of humans, animals and in many robotic applications. Besides being an interesting challenge, communication-less approaches have been applied to groups of robots in order to achieve higher scalability and simpler hardware and software design. We present a generic model and control law for robots cooperatively manipulating an object, for both ground
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ANTS 2018 special issue: Editorial Swarm Intell. (IF 2.6) Pub Date : 2019-10-28 Marco Dorigo,Mauro Birattari,Christian Blum,Anders L. Christensen,Andreagiovanni Reina,Vito Trianni
This special issue of the Swarm Intelligence journal is dedicated to the publication of extended versions of some of the best papers presented at ANTS 2018, Eleventh International Conference on Swarm Intelligence, which took place in Rome, Italy, on October 29–31, 2018. ANTS is the first and most established conference series dedicated to the dissemination of swarm intelligence research. Its first
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Sophisticated collective foraging with minimalist agents: a swarm robotics test Swarm Intell. (IF 2.6) Pub Date : 2019-10-10 Mohamed S. Talamali, Thomas Bose, Matthew Haire, Xu Xu, James A. R. Marshall, Andreagiovanni Reina
How groups of cooperative foragers can achieve efficient and robust collective foraging is of interest both to biologists studying social insects and engineers designing swarm robotics systems. Of particular interest are distance-quality trade-offs and swarm-size-dependent foraging strategies. Here, we present a collective foraging system based on virtual pheromones, tested in simulation and in swarms
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Simulation-only experiments to mimic the effects of the reality gap in the automatic design of robot swarms Swarm Intell. (IF 2.6) Pub Date : 2019-10-03 Antoine Ligot, Mauro Birattari
The reality gap—the discrepancy between reality and simulation—is a critical issue in the off-line automatic design of control software for robot swarms, as well as for single robots. It is understood that the reality gap manifests itself as a drop in performance: when control software generated in simulation is ported to physical robots, the performance observed is often disappointing compared with