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.

chapter 1|8 pages


ByAndrew Schumann

chapter 2|46 pages

Swarm Intelligence for Morphogenetic Engineering

ByBruce J. MacLennan, Allen C. McBride

chapter 3|7 pages

Ant Cemeteries as a Cluster or as an Aggregate Pile

ByTomoko Sakiyama

chapter 4|28 pages

Robust Swarm of Soldier Crabs, Mictyris guinotae, Based on Mutual Anticipation

ByY.-P. Gunji, H. Murakami, T. Niizato, Y. Nishiyama, K. Enomoto, A. Adamatzky, M. Toda, T. Moriyama, T. Kawai

chapter 5|18 pages

Swarm Intelligence in Cybersecurity

ByCong Truong Thanh, Quoc Bao Diep, Ivan Zelinka

chapter 6|19 pages

Emergence of Complex Phenomena in a Simple Reversible Cellular Space

ByKenichi Morita

chapter 7|14 pages

Rough Sets over Social Networks

ByKrzysztof Pancerz, Grochowalski Piotr

chapter 8|15 pages

Logical Functions as an Idealization of Swarm Behavior

ByAndrew Schumann

chapter 9|14 pages

On the Motion of Agents with Directional Antennae

ByAlexander Kuznetsov

chapter 10|11 pages

Induction and Physical Theory Formation As Well As Universal Computation by Machine Learning

ByAlexander Svozil, Karl Svozil