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Classification of collective behaviors in social insects. a An external view and a cross section of an Apicotermes lamani nest resulting from the coordination of workers building activities. b Collective selection of one foraging path over a diamond-shaped bridge leading to a food source by workers in the ant Lasius niger . c Weaver ant ( Oecophylla longinoda ) workers cooperate to form chains of their own bodies, allowing them to cross wide gaps and pull leaves together. d An example of division of labor among weaver ant workers ( Oecophylla longinoda ). When the leaves have been put in place by a first group of workers, both edges are connected with a thread of silk emitted by mature larvae held by a second group of workers. © CNRS Photothèque Gilles Vidal and Guy Theraulaz
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The roots of swarm intelligence are deeply embedded in the biological study of self-organized behaviors in social insects. From the routing of traffic in telecommunication networks to the design of control algorithms for groups of autonomous robots, the collective behaviors of these animals have inspired many of the foundational works in this emerg...
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... collective decisions in ants rely on self-organization that appears to be a major component of a wide range of collective behaviors in social insects, from the thermoregulation of bee swarms to the construction of nests in ants and termites (Bonabeau et al. 1997; Camazine et al. 2001). Self-organization is a set of dynamical mechanisms whereby structures appear at the global level of a system from interactions among its lower-level components, without being explicitly coded at the individual level. It relies on four basic ingredients: (1) The first component is a positive feedback that results from the execution of simple behavioral “rules of thumb” that promote the creation of structures. For instance, trail recruitment to a food source is a kind of positive feedback which creates the conditions for the emergence of a trail network at the global level. (2) Then we have a negative feedback that counterbalances positive feedback and that leads to the stabilization of the collective pattern. In the example of ant foraging, negative feedback may have several origins. It may result from the limited number of available foragers, the food source exhaustion, and the evaporation of pheromone or a competition between paths to attract foragers. (3) Self-organization also relies on the amplification of fluctuations by positive feedbacks. Social insects are well known to perform actions that can be described as stochastic. Such random fluctuations are the seeds from which structures nucleate and grow. Moreover, randomness is often crucial, because it enables the colony to discover new solutions. For instance, lost foragers can find new, unexploited food sources, and then recruit nest mates to these food sources. (4) Finally, self-organization requires multiple direct or stigmergic interactions among individuals to produce apparently deterministic outcomes and the appearance of large and enduring structures. In addition to the previously detailed ingredients, self-organization is also characterized by a few key properties: (1) Self-organized systems are dynamic. As stated before, the production of structures as well as their persistence requires permanent interactions between the members of the colony and with their environment. These interactions promote the positive feedbacks that create the collective structures and act for their subsistence against negative feedbacks that tend to eliminate them. (2) Self-organized systems exhibit emergent properties. They display properties that are more complex than the simple contribution of each agent. These properties arise from the nonlinear combination of the interactions between the members of the colony. (3) Together with the emergent properties, non linear interactions lead self-organized systems to bifurcations. A bifurcation is the appearance of new stable solutions when some of the system’s parameters change (see Appendix 1). This corresponds to a qualitative change in the collective behavior. (4) Last, self-organized systems can be multi-stable. Multi-stability means that, for a given set of parameters, the system can reach different stable states depending on the initial conditions and on the random fluctuations. From the previously described self-organizing processes may emerge a wide variety of collective behaviors that are intended to solve a given problem. Such diversity may give the impression that no common point exists at the collective level between for instance the construction of the relatively simple nest of the ant Leptothorax albipennis made up with a single wall of debris and the construction of the seemingly more complex nest of the termite Macrotermes bellicosus with its intricate network of galleries and chambers. Nevertheless, it is possible to break down all these collective behaviors into a limited number of behavioral components. For example, Anderson and Franks (2001) have proposed to separate the collective behaviors accomplished by an insect colony into four task types: individual, group, team and partitioned tasks. Following that categorization of social insects’ behaviors, Anderson et al. (2001) have proposed that every global task in a colony (for instance nest construction) can be broken down in a hierarchical structure of subtasks of the previous types. Their method can be seen as the deconstruction of a problem into the basic tasks required to solve it. Another way to deconstruct the collective behaviors of social insects goes through the functions that organize the insects’ tasks. We identified four functions of that kind: coordination, cooperation, deliberation and collaboration (see Fig. 1). They are not mutually exclu- sive but rather contribute together to the accomplishment of the various collective tasks of the colony. In the following sections, we first provide a definition of each and then illustrate their respective role in some examples of social insects’ collective ...
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... Despite the fundamental similarities between models of collective and herding movement (Vicsek & Zafeiris, 2010), there are serious challenges in capturing some of the more specific and important aspects. In particular, we argue that such models may also pertain to manifestations of rational activity, which is often interpreted as collective intelligence (McMillen & Levin, 2024) or swarm intelligence (Garnier et al., 2007). Of course, this is not "intelligence" in the full sense of the word; as such, rational behavior excludes consciousness-understood by some researchers as integrated information with a set of informational relations generated within a system (Tononi, 2008). ...
In this work, a new concept called Vector Dissipation of Randomness (VDR) is developed and formalized. It describes the mechanism by which complex multicomponent systems transition from chaos to order through the filtering of random directions, accumulation of information in the environment, and self-organization of agents. VDR explains how individual random strategies can evolve into collective goal-directed behavior, leading to the emergence of an ordered structure without centralized control.
To test the proposed model, a numerical simulation of the "ant and beetle" system was conducted, in which agents (ants) randomly choose movement directions, but through feedback mechanisms and filtering of weak strategies, they form a single coordinated vector of the beetle's movement.
VDR is a universal mechanism applicable to a wide range of selforganizing systems, including biological populations, decentralized technological networks, sociological processes, and artificial intelligence algorithms.
For the first time, an equation of the normalized emergence function in the processing of vector dissipation of randomness in the Ant and Beetle system has been formulated.
The concept of paraintelligence was introduced for the first time. Insect paraintelligence is interpreted as a rational functionality that is close to or equivalent to intelligent activity in the absence of reflexive consciousness and selfawareness.
... Despite the fundamental similarities between models of collective and herding movement (Vicsek & Zafeiris, 2010), there are serious challenges in capturing some of the more specific and important aspects. In particular, we argue that such models may also pertain to manifestations of rational activity, which is often interpreted as collective intelligence (McMillen & Levin, 2024) or swarm intelligence (Garnier et al., 2007). Of course, this is not "intelligence" in the full sense of the word; as such, rational behavior excludes consciousness-understood by some researchers as integrated information with a set of informational relations generated within a system (Tononi, 2008). ...
In this work, a new concept called Vector Dissipation of Randomness (VDR) is developed and formalized. It describes the mechanism by which complex multicomponent systems transition from chaos to order through the filtering of random directions, accumulation of information in the environment, and self-organization of agents. VDR explains how individual random strategies can evolve into collective goaldirected behavior, leading to the emergence of an ordered structure without centralized control. To test the proposed model, a numerical simulation of the "ant and beetle" system was conducted, in which agents (ants) randomly choose movement directions, but through feedback mechanisms and filtering of weak strategies, they form a single coordinated vector of the beetles movement. VDR is a universal mechanism applicable to a wide range of self-organizing systems, including biological populations, decentralized technological networks, sociological processes, and artificial intelligence algorithms. For the first time, an equation of the normalized emergence function in the processing of vector dissipation of randomness in the Ant and Beetle system has been formulated. The concept of paraintelligence was introduced for the first time. Insect paraintelligence is interpreted as a rational functionality that is close to or equivalent to intelligent activity in the absence of reflexive consciousness and selfawareness.
... One behavioral interpretation of why the field center plays a crucial role in the causal emergence of team coordination in our analysis is that it acts as a naturally emergent spatial structure that shapes collective movement. This concept aligns with stigmergy, where individuals coordinate indirectly by interacting with modifications in their environment rather than through direct communication [28]. In football, the field center provides a shared spatial reference that influences team formations and movement patterns. ...
Team dynamics significantly influence the outcomes of modern football matches. This study employs an information-theoretical approach, specifically causal emergence, combined with graph theory to explore how team-level dynamics arise from complex interactions among players, utilizing tracking data from 34 J-League matches. We focused on how collective behaviors arise from the interdependence of individual actions, examining team coordination and dynamics through player positions and movements to identify emergent properties. Specifically, we selected relative distance to the field’s center, center of mass (CoM) and clustering coefficients based on velocity similarity and inverse distance as macroscopic features to capture the key aspects of team structure, coordination, and spatial relationships. Relative distance and CoM represent the collective positioning of the team, while clustering coefficients provide insights into localized cooperation and movement similarity among the players. The results indicate that average causal emergence with relative distance and CoM as a macroscopic feature across entire games shows a strong correlation with differences in ball possession rate between home and away teams. In contrast, clustering coefficients based on inverse distance and velocity similarity showed moderate to weak correlations with ball possession rate, indicating that these metrics may capture localized interactions that are less directly tied to team-level emergent behavior compared to CoM. Additionally, relative distance and CoM as macroscopic features yield higher causal emergence in attacking phases than in defending phases before shooting, suggesting that the collective positioning of players may play a more significant role in facilitating successful attacks than in defensive stability. This study offers a novel perspective on team coordination in football, suggesting that effective team coordination may be characterized by emergent patterns arising from collective positioning. These findings have practical implications for understanding coordinated team behaviors and inform coaching and performance analysis focused on enhancing team dynamics.
... The pheromone trail strength, P pheromone , can be calculated as the sum of pheromone concentrations left by previous nanobots, represented as Eq. 6 [39]: ...
This research presents a bio-inspired nanorobot swarming algorithm based on Ant Colony Optimization (ACO) for combating Escherichia coli ( E. coli ) in Urinary Tract Infections (UTIs). UTIs caused by E. coli are a major global health concern. An approach that mimics the collective behavior of ant colonies in foraging tasks, utilizing ACO principles to guide the navigation and targeting of nanobots within the urinary tract, was proposed. Using NetLogo, an agent-based modeling platform, simulation and evaluation of the nanorobot swarm's performance was carried out. The swarm consists of autonomous agents that mimic ant behavior, with E. coli bacteria as the target. The efficacy of the ACO-based algorithm in eradicating E. coli in UTIs was demonstrated through extensive experimentation. Experimental results show that the algorithm successfully guides nanobots to locate and neutralize E. coli bacteria, reducing infection levels. The investigation also investigated the impact of parameters like pheromone evaporation rates and agent communication strategies on swarm performance. This research contributes to nanorobot technology advancement and highlights the potential of bio-inspired algorithms for medical challenges. The proposed ACO-based algorithm offers a targeted and efficient approach to combat E. coli infections in UTIs. Insights gained from this study can guide further research and development of nanorobot-based therapies for urinary tract infections and other infectious diseases.
... The study of collective behaviour is a highly interdisciplinary field of research; biologists, psychologists, physicists, complexity scientists, engineers, and others combine their expertise to understand how individuals behave in groups and how their social interactions lead to complex collective patterns (Couzin & Krause, 2003;Garnier et al., 2007;Herbert-Read, 2016;Vicsek & Zafeiris, 2012). Whether examining fish schools (Georgopoulou et al., 2022;Herbert-Read et al., 2011;Katz et al., 2011), sheep flocks (Ginelli et al., 2015;King et al., 2012), or human crowds (Ettehadieh et al., 2014;King et al., 2015;Moussaid et al., 2009), researchers often analyse data comprising multiple individuals' trajectories collected over time using motion tracking from animal-attached tags or camera images (Dell et al., 2014;Fehlmann & King, 2016;King et al., 2018). ...
... The coordinated spatial and temporal organisation of individuals seen in animal groups, whether it be fish schools or primate troops, can be studied as collective motion (Vicsek & Zafeiris, 2012), and understood from the perspective of self-organisation, where group properties emerge from individual rules of motion and interaction (Camazine et al., 2003;Garnier et al., 2007;Hemelrijk & Hildenbrandt, 2011). Collective motion is a common aspect of many collective behaviours in nature, particularly when groups move together to migrate (Voelkl et al., 2015), forage (King et al., 2008(King et al., , 2015Mazué et al., 2023) or escape predators (Herbert-Read et al., 2015;King et al., 2012;Papadopoulou et al., 2022). ...
Collective motion, that is the coordinated spatial and temporal organisation of individuals, is a core element in the study of collective animal behaviour. The self‐organised properties of how a group moves influence its various behavioural and ecological processes, such as predator–prey dynamics, social foraging and migration. However, little is known about the inter‐ and intra‐specific variation in collective motion. Despite the significant advancement in high‐resolution tracking of multiple individuals within groups, providing collective motion data for animals in the laboratory and the field, a framework to perform quantitative comparisons across species and contexts is lacking.
Here, we present the swaRmverse package. Building on two existing R packages, trackdf and swaRm, swaRmverse enables the identification and analysis of collective motion ‘events’, as presented in Papadopoulou et al. (2023), creating a unit of comparison across datasets. We describe the package's structure and showcase its functionality using existing datasets from several species and simulated trajectories from an agent‐based model.
From positional time‐series data for multiple individuals (x‐y‐t‐id), swaRmverse identifies events of collective motion based on the distribution of polarisation and group speed. For each event, a suite of validated biologically meaningful metrics are calculated, and events are placed into a ‘swarm space’ through dimensional reduction techniques.
Our package provides the first automated pipeline enabling the analysis of data on collective behaviour. The package allows the calculation and use of complex metrics for users without a strong quantitative background and will promote communication and data‐sharing across disciplines, standardising the quantification of collective motion across species and promoting comparative investigations.
... Social insects exhibit a variety of coordinated collective behaviours and accomplish social tasks. Such ability to solve group-level problems is called swarm intelligence [1], and many modelling and empirical studies revealed the individual-level behavioural mechanisms underlying social phenomena [2]. In insect societies, most such collective behaviours are performed by worker castes. ...
In social insects, individuals of working caste coordinate their actions to manage various collective tasks. Such collective behaviours exist not only in workers but also in winged reproductives (alates). During certain seasons, newly emerged alates fly from the nest to disperse and find mating partners in a synchronized manner. This ‘swarming’ behaviour is one of the collective behaviours that involve the greatest number of individuals in social insects. However, such synchronization is considered to be caused by the response to specific environmental cues rather than behavioural coordination among colony members. Here, we show that a termite Reticulitermes kanmonensis shows synchronized dispersal flight among alates within the same colony even under the constant temperature environment. Under the semi-field environment with fluctuating temperatures, alates within the same colony synchronized their dispersal flight under higher temperatures, while flight was suppressed under lower temperatures. We observed that termites synchronized their dispersal flights even under constant temperature conditions in the laboratory (20℃), indicating that environmental cues are not always necessary for synchronization. In either case, higher synchronization happened with a larger number of alates. These results suggest that social factors interplay with environmental cues to enable the synchronized swarming flight of social insects.
... Algorithms used in swarm robotics (Hamann, 2018) are usually based on relatively simple social organisms such as ants, bees, fish, etc. (Dorigo, Theraulaz, & Trianni, 2020;Garnier, Gautrais, & Theraulaz, 2007). Such algorithms try to mimic collective behavior of those species, extract the advantages that comes from decentralized way of operating and apply them to the robotic swarm. ...
This study advances collective perception in swarm robotics by introducing the Automated Swarm Opinion Diffusion Model, addressing limitations in the original Swarm Opinion Diffusion Model. While Swarm Opinion Diffusion Model integrates social and personal information for decision-making, its reliance on manually predetermined social factor parameter restricts adaptability to diverse task complexities. The novel Automated Swarm Opinion Diffusion Model eliminates this dependency by introducing an adaptive personal factor parameter, which automatically adjust the weighting of personal and social information based on the gathered information about the environment. This automated approach improves robustness and reduces the dissemination of erroneus information in early phases of the task. Comparative simulations against baseline methods (Voter model and Majority rule) demonstrate Swarm Opinion Diffusion Model's and Automated Swarm Opinion Diffusion Model's superior performance in both accuracy and consensus speed, particularly at the tasks with higher complexities. Additionally, Automated Swarm Opinion Diffusion Model maintains comparable efficiency to Swarm Opinion Diffusion Model while automating critical parameter selection, making it more suitable for real-world applications where prior knowledge about the environment and complexity of the task is unavailable. Future research will focus mostly on extending Automated Swarm Opinion Diffusion Model to best-of-n decision-making problem, where n is greater than 2, enhancing its applicability in various real-world swarm robotics scenarios.
... Swarm intelligence is a field of study that investigates the principles underlying the collective behaviors seen in nature and how these behaviors can be adapted to technology (Garnier et al., 2007;Brambilla et al., 2013). These behaviors, examples such as the nest building of complex collective ants, the honey foraging behavior of bees and the flight patterns of bird flocks, show how complex tasks can be accomplished through simple individual rules (Abdulsaheb and Kadhim, 2023). ...
This paper examines the potential implications of the principles of swarm intelligence and collective behavior in nature for unmanned systems and autonomous organizational structures. Swarm intelligence is inspired by natural systems in which individual units interact according to simple rules to form a complex and organized whole. These principles can be observed in a wide range of situations, from the synchronized flight of flocks of birds to the harmonized swimming behavior of schools of fish. The study emphasizes that swarm intelligence principles have the potential to create more flexible, resilient and efficient systems with decentralized control mechanisms and autonomous decision-making processes. Furthermore, it is suggested that these approaches can find applications in many fields, from military operations to agricultural and environmental monitoring, from disaster response to urban planning. The study provides a detailed analysis of swarm behavior in nature and discusses how these behaviors can be emulated and optimized in unmanned systems. In this context, the potential impacts of swarm intelligence and collective behavior principles on unmanned systems are evaluated in terms of increasing their adaptability, optimizing energy efficiency and maximizing mission success. It is also argued that these principles can contribute to making unmanned systems more resilient to contingencies and changing environmental conditions. This theoretical study suggests that swarm intelligence principles can provide innovative solutions for future unmanned systems and autonomous organizational structures.
... However, navigation around an obstacle on the way may require a temporary change of direction of movement for a short duration that would allow bypassing the obstacle [12]. Such emergent behaviour [43] displayed in transport phenomena is a classic example of collective animal behavior [44] and display evidence of collective intelligence [45] or, more appropriately in case of ants, swarm intelligence [46,47]. ...
Enormous progress have been made in the last 20 years since the publication of our review \cite{csk05polrev} in this journal on transport and traffic phenomena in biology. In this brief article we present a glimpse of the major advances during this period. First, we present similarities and differences between collective intracellular transport of a single micron-size cargo by multiple molecular motors and that of a cargo particle by a team of ants on the basis of the common principle of load-sharing. Second, we sketch several models all of which are biologically motivated extensions of the Asymmetric Simple Exclusion Process (ASEP); some of these models represent the traffic of molecular machines, like RNA polymerase (RNAP) and ribosome, that catalyze template-directed polymerization of RNA and proteins, respectively, whereas few other models capture the key features of the traffic of ants on trails. More specifically, using the ASEP-based models we demonstrate the effects of traffic of RNAPs and ribosomes on random and `programmed' errors in gene expression as well as on some other subcellular processes. We recall a puzzling empirical result on the single-lane traffic of predatory ants {\it Leptogenys processionalis} as well as recent attempts to account for this puzzle. We also mention some surprising effects of lane-changing rules observed in a ASEP-based model for 3-lane traffic of army ants. Finally, we explain the conceptual similarities between the pheromone-mediated indirect communication, called stigmergy, between ants on a trail and the floor-field-mediated interaction between humans in a pedestrian traffic. For the floor-field model of human pedestrian traffic we present a major theoretical result that is relevant from the perspective of all types of traffic phenomena.
... Collective motion in robotics, especially multi-wheeled robot systems and UAVs, is popular. Understanding individual agent interactions IECE Transactions on Sensing, Communication, and Control that yield coordinated collective activity is important in robotics, swarm intelligence, and social dynamics [1,2]. Multi-robot systems improve search and rescue, environmental surveillance, and logistics by clustering robots to achieve common goals. ...
Collective motion has been a pivotal area of research, especially due to its substantial importance in Unmanned Aerial Vehicle (UAV) systems for several purposes, including path planning, formation control, and trajectory tracking. UAVs significantly enhance coordination, flexibility, and operational efficiency in practical applications such as search-and-rescue operations, environmental monitoring, and smart city construction. Notwithstanding the progress in UAV technology, significant problems persist, especially in attaining dependable and effective coordination in intricate, dynamic, and unexpected settings. This study offers a comprehensive examination of the fundamental principles, models, and tactics employed to comprehend and regulate collective motion in UAV systems. This paper methodically analyses recent breakthroughs, exposes deficiencies in existing approaches, and emphasises case studies demonstrating the practical application of collective motion. The survey examines the substantial practical effects of collective motion on improving UAV operations, emphasizing scalability, resilience, and adaptability. This review is significant for its potential to inform future research and practical applications. It seeks to provide a systematic framework for the advancement of more resilient and scalable UAV collaboration models, aiming to tackle the ongoing challenges in the domain. The insights offered are essential for academics and practitioners aiming to enhance UAV collaboration in dynamic environments, facilitating the development of more sophisticated, flexible, and mission-resilient multi-UAV systems. This study is set to significantly advance UAV technology, having extensive ramifications for several industries.
