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APPLIED MATHEMATICS (92 journals)

Showing 1 - 82 of 82 Journals sorted alphabetically
Advances in Applied Mathematics     Full-text available via subscription   (Followers: 14)
Advances in Applied Mathematics and Mechanics     Full-text available via subscription   (Followers: 7)
Advances in Applied Mechanics     Full-text available via subscription   (Followers: 15)
AKCE International Journal of Graphs and Combinatorics     Open Access  
American Journal of Applied Mathematics and Statistics     Open Access   (Followers: 10)
American Journal of Applied Sciences     Open Access   (Followers: 22)
American Journal of Modeling and Optimization     Open Access   (Followers: 2)
Annals of Actuarial Science     Full-text available via subscription   (Followers: 2)
Applied Mathematical Modelling     Full-text available via subscription   (Followers: 23)
Applied Mathematics and Computation     Hybrid Journal   (Followers: 31)
Applied Mathematics and Mechanics     Hybrid Journal   (Followers: 4)
Applied Mathematics and Nonlinear Sciences     Open Access   (Followers: 1)
Applied Mathematics and Physics     Open Access   (Followers: 3)
Biometrical Letters     Open Access  
British Actuarial Journal     Full-text available via subscription   (Followers: 2)
Bulletin of Mathematical Sciences and Applications     Open Access  
Communication in Biomathematical Sciences     Open Access   (Followers: 2)
Communications in Applied and Industrial Mathematics     Open Access   (Followers: 1)
Communications on Applied Mathematics and Computation     Hybrid Journal   (Followers: 1)
Differential Geometry and its Applications     Full-text available via subscription   (Followers: 4)
Discrete and Continuous Models and Applied Computational Science     Open Access  
Discrete Applied Mathematics     Hybrid Journal   (Followers: 10)
Doğuş Üniversitesi Dergisi     Open Access  
e-Journal of Analysis and Applied Mathematics     Open Access  
Engineering Mathematics Letters     Open Access   (Followers: 1)
European Actuarial Journal     Hybrid Journal  
Foundations and Trends® in Optimization     Full-text available via subscription   (Followers: 3)
Frontiers in Applied Mathematics and Statistics     Open Access   (Followers: 1)
Fundamental Journal of Mathematics and Applications     Open Access  
International Journal of Advances in Applied Mathematics and Modeling     Open Access   (Followers: 1)
International Journal of Applied Mathematics and Statistics     Full-text available via subscription   (Followers: 3)
International Journal of Computer Mathematics : Computer Systems Theory     Hybrid Journal  
International Journal of Data Mining, Modelling and Management     Hybrid Journal   (Followers: 10)
International Journal of Engineering Mathematics     Open Access   (Followers: 6)
International Journal of Fuzzy Systems     Hybrid Journal  
International Journal of Swarm Intelligence     Hybrid Journal   (Followers: 2)
International Journal of Theoretical and Mathematical Physics     Open Access   (Followers: 13)
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems     Hybrid Journal   (Followers: 3)
Journal of Advanced Mathematics and Applications     Full-text available via subscription   (Followers: 1)
Journal of Advances in Mathematics and Computer Science     Open Access  
Journal of Applied & Computational Mathematics     Open Access  
Journal of Applied Intelligent System     Open Access  
Journal of Applied Mathematics & Bioinformatics     Open Access   (Followers: 6)
Journal of Applied Mathematics and Physics     Open Access   (Followers: 9)
Journal of Computational Geometry     Open Access   (Followers: 3)
Journal of Innovative Applied Mathematics and Computational Sciences     Open Access   (Followers: 11)
Journal of Mathematical Sciences and Applications     Open Access   (Followers: 2)
Journal of Mathematics and Music: Mathematical and Computational Approaches to Music Theory, Analysis, Composition and Performance     Hybrid Journal   (Followers: 12)
Journal of Mathematics and Statistics Studies     Open Access  
Journal of Physical Mathematics     Open Access   (Followers: 2)
Journal of Symbolic Logic     Hybrid Journal   (Followers: 2)
Letters in Biomathematics     Open Access   (Followers: 1)
Mathematical and Computational Applications     Open Access   (Followers: 3)
Mathematical Models and Computer Simulations     Hybrid Journal   (Followers: 3)
Mathematics and Computers in Simulation     Hybrid Journal   (Followers: 3)
Modeling Earth Systems and Environment     Hybrid Journal   (Followers: 1)
Moscow University Computational Mathematics and Cybernetics     Hybrid Journal  
Multiscale Modeling and Simulation     Hybrid Journal   (Followers: 2)
Pacific Journal of Mathematics for Industry     Open Access  
Partial Differential Equations in Applied Mathematics     Open Access   (Followers: 2)
Ratio Mathematica     Open Access  
Results in Applied Mathematics     Open Access   (Followers: 1)
Scandinavian Actuarial Journal     Hybrid Journal   (Followers: 2)
SIAM Journal on Applied Dynamical Systems     Hybrid Journal   (Followers: 3)
SIAM Journal on Applied Mathematics     Hybrid Journal   (Followers: 11)
SIAM Journal on Computing     Hybrid Journal   (Followers: 11)
SIAM Journal on Control and Optimization     Hybrid Journal   (Followers: 18)
SIAM Journal on Discrete Mathematics     Hybrid Journal   (Followers: 8)
SIAM Journal on Financial Mathematics     Hybrid Journal   (Followers: 3)
SIAM Journal on Imaging Sciences     Hybrid Journal   (Followers: 7)
SIAM Journal on Mathematical Analysis     Hybrid Journal   (Followers: 4)
SIAM Journal on Matrix Analysis and Applications     Hybrid Journal   (Followers: 3)
SIAM Journal on Numerical Analysis     Hybrid Journal   (Followers: 7)
SIAM Journal on Optimization     Hybrid Journal   (Followers: 12)
SIAM Journal on Scientific Computing     Hybrid Journal   (Followers: 16)
SIAM Review     Hybrid Journal   (Followers: 9)
SIAM/ASA Journal on Uncertainty Quantification     Hybrid Journal   (Followers: 2)
Swarm Intelligence     Hybrid Journal   (Followers: 3)
Theory of Probability and its Applications     Hybrid Journal   (Followers: 2)
Uniform Distribution Theory     Open Access  
Universal Journal of Applied Mathematics     Open Access   (Followers: 1)
Universal Journal of Computational Mathematics     Open Access   (Followers: 3)
Similar Journals
Journal Cover
Swarm Intelligence
Journal Prestige (SJR): 0.709
Citation Impact (citeScore): 3
Number of Followers: 3  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1935-3820 - ISSN (Online) 1935-3812
Published by Springer-Verlag Homepage  [2469 journals]
  • Emergent naming conventions in a foraging robot swarm

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      Abstract: Abstract In this study, we investigate the emergence of naming conventions within a swarm of robots that collectively forage, that is, collect resources from multiple sources in the environment. While foraging, the swarm explores the environment and makes a collective decision on how to exploit the available resources, either by selecting a single source or concurrently exploiting more than one. At the same time, the robots locally exchange messages in order to agree on how to name each source. Here, we study the correlation between the task-induced interaction network and the emergent naming conventions. In particular, our goal is to determine whether the dynamics of the interaction network are sufficient to determine an emergent vocabulary that is potentially useful to the robot swarm. To be useful, linguistic conventions need to be compact and meaningful, that is, to be the minimal description of the relevant features of the environment and of the made collective decision. We show that, in order to obtain a useful vocabulary, the task-dependent interaction network alone is not sufficient, but it must be combined with a correlation between language and foraging dynamics. On the basis of these results, we propose a decentralised algorithm for collective categorisation which enables the swarm to achieve a useful—compact and meaningful—naming of all the available sources. Understanding how useful linguistic conventions emerge contributes to the design of robot swarms with potentially improved autonomy, flexibility, and self-awareness.
      PubDate: 2022-07-13
       
  • On multi-human multi-robot remote interaction: a study of transparency,
           inter-human communication, and information loss in remote interaction

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      Abstract: Abstract 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, and robot swarms act as a semi-autonomous agents. This paper’s contribution is twofold. The first contribution is an exploration of the design space of computer-based interfaces for multi-human multi-robot operations. In particular, we focus on agent transparency and on the factors that affect inter-human communication in ideal conditions, i.e., without communication issues. Our second contribution concerns the same problem, but considering increasing degrees of information loss, defined as intermittent reception of data with noticeable gaps between individual receipts. We derived a set of design recommendations based on two user studies involving 48 participants.
      PubDate: 2022-03-11
      DOI: 10.1007/s11721-021-00209-2
       
  • Distributed deformable configuration control for multi-robot systems with
           low-cost platforms

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      Abstract: Abstract 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 requires only local information about the area around individual robots. We devise a phase transition machine, which designs overall obstacle avoidance procedures in a fully distributed way. Robots in collision with an obstacle distributively measure the topology of the sensor network formed by the robots in order to estimate the shape of the obstacle, and choose a motion model, either obstacle-detouring or bouncing-off, each of which deforms the network to avoid an obstacle without knowledge of the geometry around the obstacle. The robots then sense the maximum tree angle, which detects the straightness of a configuration to ensure the completion of the obstacle avoidance procedure, and perform flocking with a modified heading consensus to reconstruct a volumed network with their original headings. We provide theoretical performance analyses of the controller. We also validate the theoretical results by multiple simulations with a swarm with various population sizes.
      PubDate: 2022-02-26
      DOI: 10.1007/s11721-022-00211-2
       
  • Multi-guide particle swarm optimisation archive management strategies for
           dynamic optimisation problems

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      Abstract: Abstract 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 one of the objectives. It uses a bounded archive that is based on a crowding distance archive implementation. This paper adapts the MGPSO algorithm to solve DMOPs by proposing alternative archive update strategies to allow efficient tracking of the changing Pareto-optimal front. To evaluate the adapted MGPSO for DMOPs, a total of twenty-nine benchmark functions and six performance measures were implemented. The problem set consists of problems with only two or three objectives, and the exact time of the changes is assumed to be known beforehand. The experiments were run against five different environment types, where both the frequency of changes and the severity of changes parameters control how often and how severe the changes are during the optimisation of a DMOP. The best archive management approach was compared to the other state-of-the-art dynamic multi-objective optimisation algorithms (DMOAs). An extensive empirical analysis shows that MGPSO with a local search approach to the archive management achieves very competitive and oftentimes better performance when compared with the other DMOAs.
      PubDate: 2022-02-01
      DOI: 10.1007/s11721-022-00210-3
       
  • ANTS 2020 Special Issue: Editorial

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      PubDate: 2021-12-01
      DOI: 10.1007/s11721-021-00208-3
       
  • Robot swarm democracy: the importance of informed individuals against
           zealots

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      Abstract: Abstract 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 different opinions. We study the consensus in different regimes: we vary the quality of the options, the percentage of zealots and the percentage of informed versus uninformed agents. We also consider two decision mechanisms: the voter and majority rule. We study this problem using numerical simulations and mathematical models, and we validate our findings on physical kilobot experiments. We find that (1) if the number of zealots for the lowest quality option is not too high, the decision-making process is driven toward the highest quality option; (2) this effect can be improved increasing the number of informed agents that can counteract the effect of adverse zealots; (3) when the two options have very similar qualities, in order to keep high consensus to the best quality it is necessary to have higher proportions of informed agents.
      PubDate: 2021-12-01
      DOI: 10.1007/s11721-021-00197-3
       
  • Ant colony optimization for feasible scheduling of step-controlled smart
           grid generation

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      Abstract: Abstract 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 intermediate energy buffer is involved. Systematically constructing feasible solutions during optimization, hence, becomes a difficult task. We prove the NP-hardness for the example of co-generation plants and demonstrate the multi-modality of systematically designing feasible solutions. For the example of day-ahead scheduling, a model-integrated solution based on ant colony optimization has already been proposed. By using a simulation model for deciding on feasible branches, artificial ants construct the feasible search graphs on demand. Thus, the exponential growth of the graph in this combinatorial problem is avoided. We present in this extended work additional insight into the complexity and structure of the underlying the feasibility landscape and additional simulation results.
      PubDate: 2021-12-01
      DOI: 10.1007/s11721-021-00204-7
       
  • Resource ephemerality influences effectiveness of altruistic behavior in
           collective foraging

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      Abstract: Abstract 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 to non-interacting systems. In this work, we show that resource ephemerality and availability highly influence the benefits of altruistic behavior. We study an agent-based model wherein foragers can recruit others to feed on patches, instead of exploiting these individually. We show that the net gain by recruiting conspecifics can be estimated, effectively reducing the decision on patch detection to one based on a threshold. Patches with qualities above this threshold are expected to increase foraging efficiencies and should therefore induce recruiting of others. By letting foragers assume Lévy searches, we show that recruitment strategies with contrasting diffusion characteristics optimize conspecific encounter rates. Our results further indicate that active recruitment is only beneficial when patches are scarce and persistent. Most interestingly, the effect of choosing suboptimal threshold values is small over a wide range of resource ephemeralities. This suggests that the decision of whether to recruit others is more impactful than fine-tuning the recruitment decision. Finally, we show that the advantages of active recruitment depend greatly on both forager density and their interaction radius, as we observe passive strategies to be more efficient, but only when forager densities or interaction ranges are large.
      PubDate: 2021-12-01
      DOI: 10.1007/s11721-021-00205-6
       
  • Metaphor-based metaheuristics, a call for action: the elephant in the room

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      PubDate: 2021-11-30
      DOI: 10.1007/s11721-021-00202-9
       
  • Causes of variation of darkness in flocks of starlings, a computational
           model

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      Abstract: Abstract 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 birds relative to the observer rather than changes in density of the flock, but what causes other changes in darkness need to be studied still and this is the aim of the present investigation. Because we cannot empirically relate changes in darkness in flocks to quantities, such as position and orientation of the flock and of its members relative to the observer, we study this in a computational model. We use StarDisplay, a model of collective motion of starlings, because its flocks resemble empirical data in many properties, such as their three-dimensional shape, their manner of turning, the correlation of heading of its group-members, and its internal structure regarding density and stability of neighbors. We show that the change in darkness in the flocks perceived by an observer on the ground mostly depends on the observer’s distance to the flock and on the degree of exposure of the wing surface of flock members to the observer, and that darkness appears to decrease when birds roll during sharp turns. Remarkably, the darkness of the flock perceived by the observer was neither affected by the orientation of the flock relative to the observer nor by the density of the flock. Further studies are needed to investigate changes in darkness for flocks under predation.
      PubDate: 2021-11-25
      DOI: 10.1007/s11721-021-00207-4
       
  • A machine education approach to swarm decision-making in best-of-n
           problems

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      Abstract: Abstract 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, we propose the use of Machine Education (ME) as a systematic approach for designing a curriculum for teaching the agents the required skills to autonomously select appropriate behaviours. We study the use of ME in the context of decision-making in best-of-n problems. The proposed approach draws on swarm robotics expertise for identifying agents’ capabilities and limitations, the skills required for generating the desirable behaviours, and the corresponding performance measures. In addition, ME utilises ML expertise for the selection and development of the ML algorithms suitable for each skill. The results of the experimental evaluations demonstrate the superior efficacy of the ME-based approach over the state-of-the-art approaches with respect to speed and accuracy. In addition, our approach shows considerable robustness to changes in swarm size and to changes in sensing and communication noise. Our findings promote the use of ME for teaching swarm members the required skills for achieving complex swarm tasks.
      PubDate: 2021-11-22
      DOI: 10.1007/s11721-021-00206-5
       
  • Reinforcement learning as a rehearsal for swarm foraging

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      Abstract: Abstract 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 learning as a rehearsal (RLaR) framework, that we have recently introduced. We instantiate RLaR for the foraging problem and experimentally show that a key component of RLaR—a conditional probability distribution function—can be modeled as a uni-modal distribution (with a lower memory footprint) despite evidence that it is multi-modal. Our experiments also show that the learned behavior has some degree of scalability in terms of variations in the swarm size or the environment.
      PubDate: 2021-09-29
      DOI: 10.1007/s11721-021-00203-8
       
  • Discrete collective estimation in swarm robotics with distributed Bayesian
           belief sharing

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      Abstract: Abstract 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 (DMVD) to this scenario to observe their performances in a discrete collective estimation problem. We have also compared their performances against an Individual Exploration baseline. Additionally, we propose a novel collective decision-making strategy called distributed Bayesian belief sharing (DBBS) and apply it to the above discrete collective estimation problem. In the experiments, we explore the performances of considered collective decision-making algorithms in various parameter settings to determine the trade-off among accuracy, speed, message transfer and reliability in the decision-making process. Our results show that both DC and DMVD outperform the Individual Exploration baseline, but both algorithms exhibit different trade-offs with respect to accuracy and decision speed. On the other hand, DBBS exceeds the performances of all other considered algorithms in all four metrics, at the cost of higher communication complexity.
      PubDate: 2021-09-05
      DOI: 10.1007/s11721-021-00201-w
       
  • Achieving task allocation in swarm intelligence with bi-objective embodied
           evolution

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      Abstract: Abstract 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 a global fitness function. The former depends only on the behavior of an agent itself, while the latter depends on the effectiveness of cooperation among all nearby agents. We have tested two existing variants of embodied evolution on this scenario and compared their performances against those of an individual random walk baseline algorithm. We have found out that those two embodied evolutionary algorithms have good performances at the extreme cases of weight configurations, but are not adequate when the two objective functions interact. We thus propose a novel bi-objective embodied evolutionary algorithm, which handles the aforementioned scenario by controlling the proportion of specialized behaviors via a dynamic reproductive isolation mechanism. Its performances are compared against those of other considered algorithms, as well as the theoretical Pareto frontier produced by NSGA-II.
      PubDate: 2021-09-01
      DOI: 10.1007/s11721-021-00198-2
       
  • CONSOLE: intruder detection using a UAV swarm and security rings

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      Abstract: Abstract 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 (outer ring) while increases the reliability of the surveillance system featuring a last detection barrier (inner ring). Using the first return map from a chaotic attractor (an unpredictable sequence of real numbers) and a dynamic pheromone map, the UAV swarm members make a collective decision about their trajectories evaluating the options of a best-of-n problem. As a result, routes are unpredictable and detection rates are optimised. The parameters of each UAV, i.e. amount of pheromones and ring assignation, has been tuned using a specifically designed evolutionary algorithm. The performance of CONSOLE has been compared to five state-of-the-art mobility models on 20 case studies comprising 30 different scenarios each. Empirical results obtained via simulations demonstrate the better performance of CONSOLE in terms of amount of intruder detected and detection time.
      PubDate: 2021-09-01
      DOI: 10.1007/s11721-021-00193-7
       
  • Human-collective visualization transparency

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      Abstract: 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 behaviors. Traditional collective visualizations have shown all of the individual entities composing a collective, which may become problematic as collectives scale in size and heterogeneity, and tasks become more demanding. Human operators can become overloaded with information, which will negatively affect their understanding of the collective’s current state and overall behaviors, which can cause poor teaming performance. This manuscript contributes to the human-collective domain by analyzing how visualization transparency influences remote supervision of collectives. The visualization transparency analysis expands traditional transparency assessments by focusing on how operators with different individual capabilities are impacted, their comprehension, the interface usability, and the human-collective team’s performance. Metrics that effectively assess visualization transparency of collectives are identified, and design guidance can inform future real-world human-collective systems designs. The individual agent and abstract screen-based visualizations were analyzed while remotely supervising sequential best-of-n decision-making tasks involving four collectives, composed of 200 entities each, 800 in total. The abstract visualization provided better transparency by enabling operators with different individual differences and capabilities to perform relatively the same and promoted higher human-collective performance.
      PubDate: 2021-09-01
      DOI: 10.1007/s11721-021-00194-6
       
  • Collective decision-making for dynamic environments with visual occlusions

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      Abstract: Abstract 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 agents of a group are less informed than others, and their state uncertainties increase. This paper develops a new model referred to as the generalized Ising model with dynamic confidence (GIM-C) to reduce the state uncertainty induced by visual occlusions. The proposed model first estimates the expected rewards of possible actions with dynamic confidence weighting. It then gives the probability of choosing each action based on the generalized Ising model with an external field defined by the last stage’s results. Numerical simulations demonstrate that GIM-C shares the key feature of social cohesion with previous CDM models. Furthermore, in order to illustrate the efficiency of the proposed GIM-C, the collecting foraging task is considered, where a large-scale group of agents is required to obtain rewards with the presence of a dynamic predator and visual occlusions. The good performance of GIM-C in the collecting foraging task demonstrates that dynamic confidence weighting is efficient in reducing the state uncertainty introduced by visual occlusions. The proposed GIM-C also demonstrates the importance of enhancing the influence of informed agents in CDM problems in a dynamic environment with visual occlusions.
      PubDate: 2021-08-25
      DOI: 10.1007/s11721-021-00200-x
       
  • HuGoS: a virtual environment for studying collective human behavior from a
           swarm intelligence perspective

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      Abstract: Abstract 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 collectives. This requires studies with multiple human participants in controlled experiments in a wide variety of scenarios, where a rich scope of possible interactions can be isolated and captured. In this paper, we present HuGoS—‘Humans Go Swarming’—a multi-user virtual environment implemented using the Unity game development platform, as a comprehensive tool for experimentation in human swarm intelligence. We demonstrate the functionality of HuGoS with naïve participants in a browser-based implementation, in a coordination task involving collective decision-making, messaging and signaling, and stigmergy. By making HuGoS available as open-source software, we hope to facilitate further research in the field of human swarm intelligence.
      PubDate: 2021-08-03
      DOI: 10.1007/s11721-021-00199-1
       
  • Multi-featured collective perception with Evidence Theory: tackling
           spatial correlations

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      Abstract: Abstract 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 control and the main question is how to share and to combine the estimated information to achieve the most precise global estimate in the least possible time. The current article aims at answering this question with the help of evidence theory, also known as Dempster–Shafer theory, applied to the collective perception scenario as a collective decision-making problem. We study eight most common belief combination operators to address the arising conflict between different sources of evidence in a highly dynamic multi-agent setting, driven by modulation of positive feedback. In comparison with existing approaches, such as voter models, the presented framework operates on quantitative belief assignments of the agents based on the observation time of the options according to the agents’ opinions. The evaluated results on an extended benchmark set for multiple options ( \(n>2\) ) indicate that the proportional conflict redistribution (PCR) principle allows a collective of small size ( \(N=20\) ), occupying \(3.5\%\) of the surface, to successfully resolve the conflict between clustered areas of features and reach a consensus with almost \(100\%\) certainty up to \(n=5\) .
      PubDate: 2021-06-01
      DOI: 10.1007/s11721-021-00192-8
       
  • Negative updating applied to the best-of-n problem with noisy qualities

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      Abstract: Abstract 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 emphasis on small and inexpensive robots which are often equipped with low-cost sensors more prone to suffer from noisy readings. This paper presents an exploratory investigation into the robustness of a negative updating approach to the best-of-n problem which utilises negative feedback from direct pairwise comparison of options and opinion pooling. A site selection task is conducted with a small-scale swarm of five e-puck robots choosing between \(n=7\) options in a semi-virtual environment with varying levels of sensor noise. Simulation experiments are then used to investigate the scalability of the approach. We now vary the swarm size and observe the behaviour as the number of options n increases for different error levels with different pooling regimes. Preliminary results suggest that the approach is robust to noise in the form of noisy sensor readings for even small populations by supporting self-correction within the population.
      PubDate: 2021-06-01
      DOI: 10.1007/s11721-021-00188-4
       
 
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