Swarm Intelligence: From Social Bacteria to Humans

191 Pages·2020·19.907 MB·English

by  Andrew Schumann (editor)

Swarm Intelligence
From Social Bacteria to Humans

Editor
Andrew Schumann
University of Information Technology and Management
in Rzeszow, Poland
p,
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Preface

Recently, in computer science there are developed different multi-agent
systems which are inspired by intelligent behaviors of swarms: ants, bees,
flocking of sheep (horses), shoaling and schooling of fish, etc. For example,
these systems can be represented as groups of robots working together
on the same task. So, swarms are regarded as a natural kind of multi-
agent systems whose members support each other and have a division
into different social roles in realizing their joint work. Therefore, swarms
give a natural bio-inspired model of artificial multi-agent systems with
some social functions.
The aim of designing groups of agents as swarms is promising today
in the context of developing systems of Internet of Things, where artificial
objects such as computing devices, mechanical or digital machines possess
unique identifiers and can transfer data over a network without requiring
an interaction with humans. Swarm intelligence can provide the Internet
of Things with a new technology of modeling some social functions of
these things.
For instance, we may assume that the Internet of Things can possess
its own social system, where a group of objects can behave as a real swarm.
The point is that swarms effectively solve different logistic tasks. So, ants
and bees can solve the travelling salesman problem and bees can solve
the assignment problem. Hence, we can assume that the efficiency in a
self-organized realization of logistic tasks in the Internet of Things can
give a benefit in the future in designing new multi-agent systems. Hence,
swarm intelligence is an important branch of computer science helping us
in developing artificial multi-agent systems with some social functions.
This book contains results of nine researches about swarm intelligence
with an emphasis on modeling different reactions of swarms and some
ways how these models of reactions can be used in computer science.
Andrew Schumann

Contents

Preface iii
1. Introduction 1
Andrew Schumann
2. Swarm Intelligence for Morphogenetic Engineering 9
Bruce J. MacLennan and Allen C. McBride
3. Ant Cemeteries as a Cluster or as an Aggregate Pile 55
Tomoko Sakiyama
4. Robust Swarm of Soldier Crabs, Mictyris guinotae, Based on
Mutual Anticipation 62
Y.-P. Gunji, H. Murakami, T. Niizato, Y. Nishiyama, K. Enomoto,
A. Adamatzky, M. Toda, T. Moriyama and T. Kawai
5. Swarm Intelligence in Cybersecurity 90
Cong Truong Thanh, Quoc Bao Diep and Ivan Zelinka
6. Emergence of Complex Phenomena in a Simple Reversible
Cellular Space 108
Kenichi Morita
7. Rough Sets over Social Networks 127
Krzysztof Pancerz and Piotr Grochowalski
8. Logical Functions as an Idealization of Swarm Behavior 141
Andrew Schumann
9. On the Motion of Agents with Directional Antennae 156
Alexander Kuznetsov
10. Induction and Physical Theory Formation As Well As
Universal Computation by Machine Learning 170
Alexander Svozil and Karl Svozil
Index 181
About the Editor 183

1
Introduction
Andrew Schumann
Department of Cognitive Science and Mathematical Modelling,

University of Information Technology and Management in Rzeszow,

Sucharskiego 2, 35-225 Rzeszow, Poland

Email: [email protected]

The notion of swarm intelligence [6, 41] was introduced to describe the
decentralized and self-organized behaviors of groups of animals. This idea
was then extrapolated to design groups of robots which interacted locally
to cumulate a collective reaction. Some natural examples of swarms are
as follows [38]: 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.
At first, swarm intelligence was studied in order to develop new
algorithms in transporting and scheduling – the point being that ants,
bees, some social bacteria, Physarum polycephalum, etc. can solve logistic
problems very effectively [38]: (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 and by amoebae, etc.
intelligent behavior of swarm individuals is explained by the following
biological reactions to attractants and repellents [5, 14, 30, 35]. Attractants
are biologically active things, such as food pieces or sex pheromones,
which attract individuals of the swarm. Repellents are biologically active
things, such as predators, which repel individuals of the 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 such as 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%) it changes the direction, then the rest of the group follows the new
2 Swarm Intelligence: From Social Bacteria to Humans
direction, too. Some swarm patterns are observed among human beings
under the conditions addictive behavior such as the behavior of alcoholics
or gamers [38].
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.
In modeling swarms, we assume that animal collectives can contain
different numbers of their members – from a small number to a large
enough number. For example, one cluster of naked mole-rats includes on
average from 75 to 80 individuals, while there are ant colonies consisting
of many million worker ants and many thousand queen ants living in
many thousand nests. The task of simulating multi-agent systems with
many millions of actors evidently is quite hard. But standard networks,
we deal with in our life, such as social networks contain so many active
individuals, too.
A neuromorphic computer, to which we have devoted the first
contribution to this book entitled Swarm Intelligence for Morphogenetic
Engineering, can be represented as a swarm with a huge number of
active components. For designing this computer we should set 100
billion neurons and define 100 trillion nonrandom connections among
them. These neurons are regarded by Bruce J. MacLennan and Allen
C. McBride (the two authors of the chapter) as separate microscopic
agents (microrobots) that can emit and respond to simple signals and
implement simple control processes, but they can also move and transport
other components. Signaling molecules and structural components are
considered passive components, because they cannot move without
external forces. Microrobots as active components take part in an artificial
morphogenesis through assembling passive components into a desired
structure. This morphogenesis is a result of interactions of collectives
of microrobots.
In this chapter, Bruce J. MacLennan and Allen C. McBride proposed
certain algorithms for the coordination of microrobot swarms involved in
morphogenetic engineering. The morphogenetic programming notation
for this purpose is based on a mathematical notation developed for partial
differential equations. For more details on neuromorphic computers with
artificial morphogenesis, please see [24, 25, 26, 27, 28, 29]. Hence, the ideas
of Bruce J. MacLennan and Allen C. McBride appeals to the modeling and
controlling of artificial swarms consisting of millions microrobots.
Some computational tasks which are being solved by swarms
effectively such as transporting and scheduling are studied in depth.
Nevertheless, there are many sophisticated tasks solved by swarms daily
Introduction 3
which are little known in computer science, yet. For instance, among social
insects we can observe necrophoresis – a social phenomenon of carrying
dead bodies of members of colonies of ants or bees from the nest [8, 15].
In the second contribution to the book under the title Ant Cemeteries as a
Cluster or as an Aggregate Pile prepared by Tomoko Sakiyama, there is an
examination of a formation of cemeteries performed by ant workers. This
social behavior can be formalized by some simple clustering rules such
as the following implication: if ant workers find a corpse, then they pick
up it with a probability that decreases according to the cluster size, while
corpse-carrying ants drop their carrying corpses with a probability that
increases due to the cluster size. On the basis of these rules, ant workers
build large piles of corpses [37, 40]. In the model proposed by Tomoko
Sakiyama, agents can modify the probability of the drop, which was
dependent on whether they detected or did not detect their nest-mates.
Therefore, this chapter shows that swarms of ants comply with many
objectives simultaneously from looking for food to building cemeteries.
In a flock of birds and school of fish, individuals try to coordinate
their behavior on the basis of their neighbors to avoid collisions with
them. However, there are many examples of group behavior without this
mechanism. The soldier crabs of Mictyris guinotae behave as a swarm with
an internal noise and/or anticipation. In the chapter Robust Swarm of Soldier
Crabs, Mictyris guinotae, Based on Mutual Anticipation which was prepared
by Y.-P. Gunji, H. Murakami, T. Niizato, Y. Nishiyama, K. Enomoto, A.
Adamatzky, M. Toda, T. Moriyama and T. Kawai, there is a kinetic model
analyzing how crabs move, revealing dynamic internal structures within
groups, such as topological distances, scale-free correlations and inherent
noise. To learn more about this model, see [12, 13].
In computer science, there are in general some basic patterns
formalized of different swarms: Ant Colony Optimization [9, 10, 11],
Artificial Bee Colony [16, 17, 18], Particle Swarm Optimization [19, 20],
etc. The algorithms of Ant Colony Optimization and Artificial Bee Colony
are aimed, first of all, for solving logistic problems such as scheduling,
assignment, and transport. The algorithms of Particle Swarm Optimization
allows us to simulate the group movement of animals with collision
avoidance (individuals avoid a collision with neighbors), velocity matching
(individuals synchronize their speed with their neighbors), and swarm
centering (individuals stay close to their neighbors). All these algorithms
formalizing the fundamental swarm patterns of animals can be applied
in different areas of computer science: from designing artificial swarms
of robots to cybersecurity. In the contribution to our book entitled Swarm
intelligence in Cybersecurity submitted by Cong Truong Thanh, Quoc Bao
Diep, and Ivan Zelinka, there are analyzed prospects of applying swarm
intelligence techniques such as Ant Colony Optimization, Particle Swarm
Optimization, and S-Cuckoo Search in combating cyber-attacks [2, 3, 21].