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In biological and social systems, a "swarm" refers to a group of individual units that behave as a single intelligent entity. "Swarm behavior," the collective result of the local interactions among the group members, exhibits what is called "swarm intelligence." By identifying the design principles of such swarm intelligence, we may be able to create swarm robots that are highly adaptable, fault tolerant, and dimensionally flexible. An interdisciplinary approach, including disciplines ranging from technology to biology to the mathematical sciences, for example, is used to elucidate the design principles of swarm intelligence. We believe that such knowledge will lead to transformations in the field of swarm robotics. This special issue highlights 19 exciting papers, including 13 research papers, five review papers, and one letter. Some papers focus on understanding the mechanism of real swarm phenomena, while the other papers focus on designing intelligent swarm systems. The keywords of the papers are as follows. • Swarm intelligence • Interdisciplinary approach • Decentralized control • Swarm robot • Collective behavior We would like to express our gratitude to all authors and reviewers, and we hope that this special issue contributes to future research and development in swarm intelligence. [ABSTRACT FROM AUTHOR]

This paper presents a method of generating collective behavior of a multi-legged robotic swarm using deep reinforcement learning. Most studies in swarm robotics have used mobile robots driven by wheels. These robots can operate only on relatively flat surfaces. In this study, a multi-legged robotic swarm was employed to generate collective behavior not only on a flat field but also on rough terrain fields. However, designing a controller for a multi-legged robotic swarm becomes a challenging problem because it has a large number of actuators than wheeled-mobile robots. This paper applied deep reinforcement learning to designing a controller. The proximal policy optimization (PPO) algorithm was utilized to train the robot controller. The controller was trained through the task that required robots to walk and form a line. The results of computer simulations showed that the PPO led to the successful design of controllers for a multi-legged robotic swarm in flat and rough terrains. [ABSTRACT FROM AUTHOR]

This article provides comparative perspectives on collective behaviors that are widely found throughout the animal kingdom, ranging from insect and crustacea swarms, fish schools, bird flocks, and mammal herds to human crowds. Studies of nonhuman animal and human collective behaviors have progressed almost separately even though they have a similar history. Theoretical studies have investigated the reproduction of collective phenomena from simple inter-individual rules, and subsequent empirical and experimental studies have found diverse and complex collective behaviors that are difficult to explain with classical theoretical models. As a consequence, a wide variety of interaction rules have been proposed. To determine models to be implemented in nature and find fundamental mechanisms of collective behaviors, this paper argues that we should compare collective behaviors among various species while adopting Tinbergen's four questions regarding mechanism, function, development, and evolution as a methodological basis. As an example of a comparative collective behavior paradigm, we introduce our studies in which a mutual anticipation mechanism inspired by nonhuman animal collective behaviors can be linked to a self-organization function in human collective behaviors. We expect that the study of comparative collective behaviors will expand, the methodology will become more sophisticated, and new perspectives regarding the multitemporal features of collective behaviors will emerge. [ABSTRACT FROM AUTHOR]

In the studies done to date on the swarm behaviors of animals, many different observational techniques have been developed, indicating the importance of such detailed observations. The techniques of researchers aiming to capture the swarm behavior of animals, which is normally visually unobservable, have included attaching microsensors to honey bees or ants and data loggers (micro recorders) to birds or mammals. Such techniques, collectively known as "bio-logging," can go far in clarifying why we feel animals that exhibit swarm behaviors seem to have a sort of collective intelligence, or "swarm intelligence." Furthermore, studies on the swarm behaviors of animals may provide important clues to researchers in the field of swarm robotics. It is in this context that this special issue presents papers on bio-logging technologies, the collective behaviors of animals, and various advanced measurement technologies related to them. This special issue consists of one review article and 14 research papers. The subjects cover a wide range of areas, including control engineering, data science, and ecology. Thus, bio-logging is an interdisciplinary area that can expect to see much growth in the near future. The editors are confident that this issue will greatly contribute to further progress in the field of bio-logging. [ABSTRACT FROM AUTHOR]

Swarm robotic systems (SRSs) are a type of multirobot system in which robots operate without any form of centralized control. The typical design methodology for SRSs comprises a behavior-based approach, where the desired collective behavior is obtained manually by designing the behavior of individual robots in advance. In contrast, in an automatic design approach, a certain general methodology is adopted. This paper presents a deep reinforcement learning approach for collective behavior acquisition of SRSs. The swarm robots are expected to collect information in parallel and share their experience for accelerating their learning. We conducted real swarm robot experiments and evaluated the learning performance of the swarm in a scenario where the robots consecutively traveled between two landmarks. [ABSTRACT FROM AUTHOR]

This paper focuses on the effect of congestion on swarm performance by considering the number of robots and their size. Swarm robotics is the study of a large group of autonomous robots from which collective behavior emerges without reliance on any centralized control. Due to the fact that robotic swarms are composed of a large number of robots, it is important to consider the congestion among them. However, only a few studies have focused on the relationship between the congestion and the performance of robotic swarms;moreover, these studies only discuss the effect of the number of robots. In this study, experiments were conducted by computer simulation and carried out by varying both the number of robots and the size of the robots in a path formation task. The robot controller was designed with an evolutionary robotics approach. The results show that not only the number of robots but also their size are essential features in the relationship between congestion and swarm performance. In addition, autonomous specialization within the robotic swarm emerged in situations with moderate congestion. [ABSTRACT FROM AUTHOR]

Studying collective behavior in opposing multi-agent teams is crucial across game theory, robotics, and sports analytics. In sports, especially football, team tactics involve intricate strategic spatial and action behaviors displayed as event sequences during possession. Understanding and analyzing these tactics is essential for successful training, strategic planning, and on-field success. While traditional approaches, such as notational and statistical analyses, offer valuable insights into team tactics, they often lack a comprehensive consideration of contextual information, thereby limiting the holistic evaluation of teams' performances. To bridge this gap and capture the nuanced intricacies of team tactics, we employed advanced methodologies. The sequential pattern mining algorithm PrefixSpan was utilized to extract tactical patterns from possession sequences, enabling a deeper understanding of how teams strategize and adapt during play. Additionally, the neural marked spatio temporal point process (NMSTPP) model was leveraged to model and predict team behaviors, facilitating a fair comparison among teams. The evaluation of team possessions was further enhanced through the innovative holistic possession utilization score metrics, providing a more nuanced assessment of performance. In our experimental exploration, we identified and classified five distinct team tactics, validated the efficacy of the NMSTPP model when integrating StatsBomb 360 data, and conducted a comprehensive analysis of English Premier League teams during the 2022/2023 season. The results were visualized using radar plots and scatter plots with mean shift clustering. Lastly, the potential applications to RoboCup were discussed. [ABSTRACT FROM AUTHOR]

Swarm robotics is the study of how a large number of relatively simple physically embodied robots can be designed such that a desired collective behavior emerges from local interactions. Furthermore, reinforcement learning (RL) is a promising approach for training robotic swarm controllers. However, the conventional RL approach suffers from the sparse reward problem in some complex tasks, such as key-to-door tasks. In this study, we applied hierarchical imitation learning to train a robotic swarm to address a key-to-door transport task with sparse rewards. The results demonstrate that the proposed approach outperforms the conventional RL method. Moreover, the proposed method outperforms the conventional hierarchical RL method in its ability to adapt to changes in the training environment. [ABSTRACT FROM AUTHOR]

Urban centers are getting crowded, public transportation is becoming congested, and mass events are attracting an increasing number of people. Crowd disasters are not rare, and to prevent them the careful planning of pedestrian facilities and collaboration among stakeholders in the organization of events are crucial. When communication and coordination among stakeholders are sufficient, safety can usually be achieved; however, even in such cases, unexpected situations may occur. Automated crowd-control methods are required to address such situations. However, little is known about how crowd behavior can be influenced without direct human intervention. In this study, we investigated the use of environmental stimuli to modify pedestrian behavior (more specifically, route choice) in an educational facility. Colors, lights, signs, and sounds were used to influence route selection. The results show that light and, in part, LED information displays are somehow effective and could be valid candidates to pave the way for automated crowd control systems (especially for night events). The experiment presented here considers low crowd density. However, we believe that this could help encourage the balanced use of space by pedestrians under normal conditions and establish good practices. In turn, this can delay the creation of high densities, which are often the cause of fatalities in crowd disasters, and provide staff with time for intervention. [ABSTRACT FROM AUTHOR]

Human crowd behavior is an intriguing example of collective behavior where global dynamics emerge from interindividual interactions. Although most studies of human crowds assume that all individuals are independent, the presence and impact of social groups, such as friends or families, have recently been explored. Spatial structures resulting from group behavior and decision-making processes are key aspects of social groups, and how such structures are formed and the impact of these are largely unknown, especially in the scenario of crowd evacuations. Here we experimentally show that in the vicinity of exits, dyads and triads form a representative formation, which would attenuate a disadvantage of small groups that are typically regarded as moving obstacles toward the evacuation dynamics. In laboratory experiments on crowd evacuations regarding small group behavior, we first observed no noticeable differences of egress time between monads and groups, which is contrary to a naïve intuitiveness but in agreement with recent experimental observations. To resolve this contradiction, a detailed analysis focusing on behavior in front of exits suggests that group members constitute a straight-line structure vertical to the exit. Our findings offer a new perspective on how adaptive small group behavior emerges during crowd evacuation. [ABSTRACT FROM AUTHOR]

Understanding how visitors respond to crowding is crucial for ensuring the success, safety, and overall enjoyment of large-scale exhibitions. However, despite its significance, limited research has explored this issue. To address this gap, we conducted a questionnaire survey in 2022 and 2023 for visitors to exhibitions held at the Tokyo Big Sight Convention Center. Our research aims to explore the level of consistency between perceived and actual crowding, levels of discomfort in crowded situations, tolerance levels for crowding, and behaviors to avoid crowded situations. The results revealed significant changes in visitor perceptions across the two-year period. Notably, the acceptable crowd density in 2023 increased by 13% compared to 2022. This change in level of tolerance for crowding might have been influenced by the COVID-19 pandemic and other contextual factors that altered visitors' perceptions toward crowding. Interestingly, despite these perceptual changes, behaviors to avoid crowded situations remained consistent across both years. The most common response was that no special action was taken to avoid crowded situations. These findings underscore that perceptions and behaviors related to crowding in the context of large-scale exhibitions are not uniform. Our findings offer several implications and insights for crowd management, which event organizers can use to devise strategies aimed at enhancing visitors' experiences and effectively managing crowding in large-scale exhibitions. [ABSTRACT FROM AUTHOR]

Swarm intelligence is intelligence produced by multiple agents interacting with each other according to a simple set of rules, resulting in a system-wide intelligence. Such intelligence is found in a wide range of biological and social systems, and attempts have been made to understand the underlying principles through analytical approaches by biologists and sociologists and synthetic approaches by mathematical scientists and engineers. On the other hand, there are also attempts to construct artificial swarm intelligence systems that are not necessarily based on real-world phenomena. This review describes recent interdisciplinary research on swarm intelligence and its future prospects. [ABSTRACT FROM AUTHOR]

In this study, we added voting behavior in which voting proportionately reflects the value of a view (option, opinion, and so on) to the BRT agent. BRT agent is a consensus-building model of the decision-making process among a group of human, and is a framework that allows the expression of the collective behavior while maintaining dispersiveness, although it has been noted that it is unable to reach consensus by making use of experience. To resolve this issue, we propose the incorporation of a mechanism of voting at frequencies proportional to the value estimated using reinforcement learning. We conducted a series of computer-based experiments using the box-pushing problem and verified that the proposed method reached a consensus to arrive at solutions based on experience. [ABSTRACT FROM AUTHOR]

We propose a pose estimation method using a National Advisory Committee for Aeronautics (NACA) airfoil model for fish schools. This method allows one to understand the state in which fish are swimming based on their posture and dynamic variations. Moreover, their collective behavior can be understood based on their posture changes. Therefore, fish pose is a crucial indicator for collective behavior analysis. We use the NACA model to represent the fish posture; this enables more accurate tracking and movement prediction owing to the capability of the model in describing posture dynamics. To fit the model to video data, we first adopt the DeepLabCut toolbox to detect body parts (i.e., head, center, and tail fin) in an image sequence. Subsequently, we apply a particle filter to fit a set of parameters from the NACA model. The results from DeepLabCut, i.e., three points on a fish body, are used to adjust the components of the state vector. This enables more reliable estimation results to be obtained when the speed and direction of the fish change abruptly. Experimental results using both simulation data and real video data demonstrate that the proposed method provides good results, including when rapid changes occur in the swimming direction. [ABSTRACT FROM AUTHOR]

Echolocating bats perceive their surroundings by listening to the echoes of self-generated ultrasound pulses. When multiple conspecifics fly in close proximity to each other, sounds emitted from nearby individuals could mutually interfere with echo reception. Many studies suggest that bats employ frequency shifts to avoid spectral overlap of pulses with other bats. Technical constraints in recording technology have made it challenging to capture subtle changes in the pulse characteristics of bat calls. Therefore, how bats change their behavior to extract their own echoes in the context of acoustic interference remains unclear. Also, to our best knowledge, no studies have investigated whether individual flight paths change when other bats are present, although movements likely reduce acoustic masking. Here, we recorded the echolocation pulses of bats flying alone or in pairs using telemetry microphones. Flight trajectories were also reconstructed using stereo camera recordings. We found no clear tendency to broaden individual differences in the acoustic characteristics of pulses emitted by pairs of bats compared to bats flying alone. However, some bats showed changes in pulse characteristics when in pairs, which suggests that bats can recognize their own calls based on the initial differences in call characteristics between individuals. In addition, we found that the paired bats spend more time flying in the same directions than in the opposite directions. Besides, we found that the flight paths of bats were more similar in "paired flight trials" than in virtual pairs of paired flight trials. Our results suggest that the bats tend to follow the other bat in paired flight. For the following bat, acoustic interference may be reduced, while the opportunity to eavesdrop on other bats' calls may be increased. [ABSTRACT FROM AUTHOR]

Echolocating bats perceive the surrounding environment by processing echoes of their ultrasound emissions. Echolocation enables bats to avoid colliding with external objects in complete darkness. In this study, we sought to develop a method for measuring the collective behavior of echolocating bats (Miniopterus fuliginosus) emerging from their roost cave using high-sensitivity stereo-camera recording. First, we developed an experimental system to reconstruct the three-dimensional (3D) flight trajectories of bats emerging from the roost for nightly foraging. Next, we developed a method to automatically track the 3D flight paths of individual bats so that quantitative estimation of the population in proportion to the behavioral classification could be conducted. Because the classification of behavior and the estimation of population size are ecologically important indices, the method established in this study will enable quantitative investigation of how individual bats efficiently leave the roost while avoiding colliding with each other during group movement and how the group behavior of bats changes according to weather and environmental conditions. Such high-precision detection and tracking will contribute to the elucidation of the algorithm of group behavior control in creatures that move in groups together in three dimensions, such as birds. [ABSTRACT FROM AUTHOR]