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Swarm Intelligence: From Social Bacteria to Human Beings

date: January 8th, 2019

Editor: Andrew Schumann

 to be published by CRC Press, Taylor & Francis Group.


The notion of swarm intelligence is used to denote the collective behavior of decentralized and self-organized systems. Now, this notion is used in robotics to design a population of robots interacting locally among themselves and reacting locally to their environment with an emergent effect when all the local reactions of them are being cumulated into a collective reaction. There are many natural examples of swarm intelligence: ant colonies, bee colonies, fish schooling, bird flocking and horse herding, bacterial colonies with a kind of social behavior, multinucleated giant amoebae Physarum polycephalum, etc. The main feature of all these systems is that their individual agents behave locally without any centralized control, but their interactions lead to the emergence of global behaviour of the whole group that cannot be reduced to subsystems additively.

The swarm intelligence is actively studied now, because swarms (ants, bees, some social bacteria, Physarum polycephalum, etc.) can solve logistic and transport problems very effectively. For instance, there is a collective navigation of bacterial swarms and there is an effective path finding by amoebae and a possibility of traffic optimization by them. Swarms can easily solve some complex (NP-hard) logistic problems: (i) the Travelling Salesman Problem can be solved by ants and by amoebae; (ii) the Steiner Tree Problem can be solved by amoebae; (iii) the Generalized Assignment Problem can be solved by bees; (iv) mazes can be solved by ants, etc. As we see, even unicellular organisms can solve logistic problems effectively. Also, they can be involved in constructing algorithms for simulating the crowd evacuation and for simulating transport systems.

The main characteristics of any swarm consists in a possibility to optimizing the own traffic in reactions to attractants and repellents. Attractants are things or sites in the environment, such as food pieces and sex pheromones, which attract individuals of swarm. Repellents are things or sites in the environment, such as predators, which repel individuals of swarm. Hence, attractants and repellents stimulate the directed movement of swarms towards and away from the stimulus, respectively. Various compounds can act as attractants and repellents and they are different for different swarms. For the social bacteria, nutrients (including sugars, such as maltose, ribose, galactose, and amino acids, such as L-aspartate and L-serine) are attractants and some chemical conditions damaging to bacteria (including the high concentration of salts or weak acids) are their repellents. For different insects, there are many different sex attractants produced by males or females as well as feeding stimulants and attractants produced by plants and there are many different repellents produced by arthopods or repellents found in plants.

A group of people, such as pedestrians, can also exhibit a swarm behavior like a flocking or schooling: humans prefer to avoid a person conditionally designated by them as a possible predator and if a substantial part of the group (not less than 5%) changes the direction, then the rest follows the new direction. Under the conditions of escape panic the majority of people perform a swarm behavior, too. The point is that a risk of predation is the main feature of swarming at all and under these risk conditions (like a terrorist act) symbolic meanings for possible human interactions are promptly reduced. As a consequence, the social behavior transforms into a swarm behavior. Also, the swarm behavior is observed among human beings under the conditions of addictive behavior such as the behavior of alcoholics or gamers – in this case the role of human attractants causing addiction increases strongly.

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.

Potential topics include but are not limited to the following:

·        Theoretical results from the point of view of computer science

o   Swarm computing

o   Social bacteria computing

o   Social insects computing

o   Slime mould computing

o   Ad hoc and sensor wireless network

o   Bio-molecular computing

o   Computational models of swarm cognition

o   Social Networks

·        Theoretical results from the point of view of cognitive science

o   Biochemestry of swarm behavior

o   Social structure of swarm behavior

o   Psychology of swarm behavior

o   Crowd psychology

o   Crowd behavior

o   Group behavior of addicted people

o   Group behavior under risk conditions

o   Simulations of swarm behavior

 Please submit the draft of chapter to Andrew.Schumann @


Submission Deadline

20 May 2019

Acceptance Notification

20 June 2019