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Swarm intelligence

What is swarm intelligence?

​​Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. 

Swarm Intelligence systems consist typically of a population of simple agents interacting locally with one another and with their environment. The inspiration often comes from nature, especially biological systems. 

The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of "intelligent" global behavior, unknown to the individual agents. Examples of swarm intelligence in natural systems include ant colonies, bee colonies, bird flocking, hawks hunting, animal herding, bacterial growth, fish schooling, and microbial intelligence.

What are the properties of a swarm intelligence system?

The typical swarm intelligence system has the following properties:

  • It is composed of many individuals
  • The individuals are relatively homogeneous (i.e., they are either all identical or they belong to a few typologies)
  • The interactions among the individuals are based on simple behavioral rules that exploit only local information that the individuals exchange directly or via the environment 
  • The overall behaviour of the system results from the interactions of individuals with each other and with their environment, that is, the group behavior self-organizes.

The characterizing property of a swarm intelligence system is its ability to act in a coordinated way without the presence of a coordinator or of an external controller. Many examples can be observed in a nature of swarms that perform some collective behavior without any individual controlling the group, or being aware of the overall group behavior. Notwithstanding the lack of individuals in charge of the group, the swarm as a whole can show intelligent behavior. This is the result of the interaction of spatially neighboring individuals that act on the basis of simple rules.

Most often, the behavior of each individual of the swarm is described in probabilistic terms: Each individual has a stochastic behavior that depends on his local perception of the neighborhood.

What are the features of a swarm intelligence system?

Because of the above properties, it is possible to design swarm intelligence systems that are scalable, parallel, and fault-tolerant.

1. Scalability

Scalability means that a system can maintain its function while increasing its size without the need to redefine the way its parts interact. Because in a swarm intelligence system interactions involve only neighboring individuals, the number of interactions tends not to grow with the overall number of individuals in the swarm: each individual's behavior is only loosely influenced by the swarm dimension. In artificial systems, scalability is interesting because a scalable system can increase its performance by simply increasing its size, without the need for any reprogramming.

2. Parallel

Parallel action is possible in swarm intelligence systems because individuals composing the swarm can perform different actions in different places at the same time. In artificial systems, parallel action is desirable because it can help to make the system more flexible, that is, capable to self-organize in teams that take care simultaneously of different aspects of a complex task.

3. Fault-tolerance

Fault tolerance is an inherent property of swarm intelligence systems due to the decentralized, self-organized nature of their control structures. Because the system is composed of many interchangeable individuals and none of them is in charge of controlling the overall system behavior, a failing individual can be easily dismissed and substituted by another one that is fully functioning.

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Swarm intelligence

October 14, 2020

Table of contents

Key takeawaysCollaboration platforms are essential to the new way of workingEmployees prefer engati over emailEmployees play a growing part in software purchasing decisionsThe future of work is collaborativeMethodology

What is swarm intelligence?

​​Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. 

Swarm Intelligence systems consist typically of a population of simple agents interacting locally with one another and with their environment. The inspiration often comes from nature, especially biological systems. 

The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of "intelligent" global behavior, unknown to the individual agents. Examples of swarm intelligence in natural systems include ant colonies, bee colonies, bird flocking, hawks hunting, animal herding, bacterial growth, fish schooling, and microbial intelligence.

What are the properties of a swarm intelligence system?

The typical swarm intelligence system has the following properties:

  • It is composed of many individuals
  • The individuals are relatively homogeneous (i.e., they are either all identical or they belong to a few typologies)
  • The interactions among the individuals are based on simple behavioral rules that exploit only local information that the individuals exchange directly or via the environment 
  • The overall behaviour of the system results from the interactions of individuals with each other and with their environment, that is, the group behavior self-organizes.

The characterizing property of a swarm intelligence system is its ability to act in a coordinated way without the presence of a coordinator or of an external controller. Many examples can be observed in a nature of swarms that perform some collective behavior without any individual controlling the group, or being aware of the overall group behavior. Notwithstanding the lack of individuals in charge of the group, the swarm as a whole can show intelligent behavior. This is the result of the interaction of spatially neighboring individuals that act on the basis of simple rules.

Most often, the behavior of each individual of the swarm is described in probabilistic terms: Each individual has a stochastic behavior that depends on his local perception of the neighborhood.

What are the features of a swarm intelligence system?

Because of the above properties, it is possible to design swarm intelligence systems that are scalable, parallel, and fault-tolerant.

1. Scalability

Scalability means that a system can maintain its function while increasing its size without the need to redefine the way its parts interact. Because in a swarm intelligence system interactions involve only neighboring individuals, the number of interactions tends not to grow with the overall number of individuals in the swarm: each individual's behavior is only loosely influenced by the swarm dimension. In artificial systems, scalability is interesting because a scalable system can increase its performance by simply increasing its size, without the need for any reprogramming.

2. Parallel

Parallel action is possible in swarm intelligence systems because individuals composing the swarm can perform different actions in different places at the same time. In artificial systems, parallel action is desirable because it can help to make the system more flexible, that is, capable to self-organize in teams that take care simultaneously of different aspects of a complex task.

3. Fault-tolerance

Fault tolerance is an inherent property of swarm intelligence systems due to the decentralized, self-organized nature of their control structures. Because the system is composed of many interchangeable individuals and none of them is in charge of controlling the overall system behavior, a failing individual can be easily dismissed and substituted by another one that is fully functioning.

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