Recently, there has been an increasing focus on various biological and physical systems known as "active matter". Examples of such systems range from individual units, such as motile cells or artificial self-propelled particles, to large systems of interacting active particles or individuals. The emergence of large-scale collective motion, as exhibited by flocks of birds or bacterial colonies, is just one prominent and fascinating example of self-organization in active matter systems. In this work, we discuss different individual-based models of active matter using the concept of active Brownian motion. The first part of this work explores the dynamical behavior of single active particles with a particular emphasis on the impact of so-called active fluctuations. The second part extends the scope of this study to interacting active Brownian particles and their collective behavior. First, a systematic derivation of kinetic equations for active Brownian particles with velocity alignment is presented. Further on, motivated by recent biological observations, a new type of "escape-pursuit" model of collective motion is introduced and successfully employed in modeling collective locust behavior.

This book lays out a vision for a coherent framework for understanding complex systems. By developing the genuine idea of Brownian agents, the author combines concepts from informatics, such as multiagent systems, with approaches of statistical many-particle physics. It demonstrates that Brownian agent models can be successfully applied in many different contexts, ranging from physicochemical pattern formation to swarming in biological systems.

The notion of swarm intelligence was introduced for describing decentralized and self-organized behaviors of groups of animals. Then this idea was extrapolated to design groups of robots which interact locally to cumulate a collective reaction. Some natural examples of swarms are as follows: ant colonies, bee colonies, fish schooling, bird flocking, horse herding, bacterial colonies, multinucleated giant amoebae Physarum polycephalum, etc. In all these examples, individual agents behave locally with an emergence of their common effect. An intelligent behavior of swarm individuals is explained by the following biological reactions to attractants and repellents. Attractants are biologically active things, such as food pieces or sex pheromones, which attract individuals of swarm. Repellents are biologically active things, such as predators, which repel individuals of swarm. As a consequence, attractants and repellents stimulate the directed movement of swarms towards and away from the stimulus, respectively. It is worth noting that a group of people, such as pedestrians, follow some swarm patterns of flocking or schooling. For instance, humans prefer to avoid a person considered by them as a possible predator and if a substantial part of the group in the situation of escape panic (not less than 5%) changes the direction, then the rest follows the new direction, too. Some swarm patterns are observed among human beings under the conditions of their addictive behavior such as the behavior of alcoholics or gamers. The methodological framework of studying swarm intelligence is represented by unconventional computing, robotics, and cognitive science. In this book we aim to analyze new methodologies involved in studying swarm intelligence. We are going to bring together computer scientists and cognitive scientists dealing with swarm patterns from social bacteria to human beings. This book considers different models of simulating, controlling, and predicting the swarm behavior of different species from social bacteria to humans.

This book is focused on the attractive emerging field of micro-/nanorobot swarms (microswarms). It introduces fundamental understandings of various microswarms, including pattern generation, transformation, locomotion, and imaging. This book also demonstrates applications of micro-/nanorobot swarms in different fields, such as biomedical, environmental, and electrical applications. The detailed theoretical analysis and experimental demonstrations in this book provide readers ranging from students to researchers with a realistic picture of progress achieved in the field of micro-/nanorobot swarms. ​

This book presents fundamental theoretical results for designing object-oriented programming languages for controlling swarms. It studies the logics of swarm behaviours. According to behaviourism, all behaviours can be controlled or even managed by stimuli in the environment: attractants (motivational reinforcement) and repellents (motivational punishment). At the same time, there are two main stages in reactions to stimuli: sensing (perceiving signals) and motoring (appropriate direct reactions to signals). This book examines the strict limits of behaviourism from the point of view of symbolic logic and algebraic mathematics: how far can animal behaviours be controlled by the topology of stimuli? On the one hand, we can try to design reversible logic gates in which the number of inputs is the same as the number of outputs. In this case, the behaviouristic stimuli are inputs in swarm computing and appropriate reactions at the motoring stage are its outputs. On the other hand, the problem is that even at the sensing stage each unicellular organism can be regarded as a logic gate in which the number of outputs (means of perceiving signals) greatly exceeds the number of inputs (signals).