What is swarm intelligence and why is it used?
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 being used in natural systems include ant colonies, bee colonies, bird flocking, hawks hunting, animal herding, bacterial growth, fish schooling, and microbial intelligence.
There are several applications for techniques that are based on swarm intelligence. Swarm principles are even used in robotics. It’s application in the field of robotics is known as swarm robotics. The concept of swarm intelligence, however, focuses to a greater extent on the more general set of algorithms. Swarm prediction has also been employed in the context of forecasting problems. There are approaches quite similar to those proposed for swarm robotics being considered for genetically modified organisms in synthetic collective intelligence.
The US military has been considering swarm techniques for controlling unmanned vehicles, the European Space Agency is theorizing an orbital swarm for self-assembly and interferometry, and NASA is even looking into the use of swarm technology for the purpose of planetary mapping. There has even been a paper released in 1992 by M. Anthony Lewis and George A. Bekey that talks about the possibility of utilizing swarm intelligence to control nanobots within the body in order to kill cancer tumors.
Swarm intelligence has even been used in data mining and cluster analysis.
There has even been research performed on the use of swarm intelligence in telecommunication networks through ant-based routing. It makes use of a probabilistic routing table that rewards or reinforces the route successfully traversed by every "ant" (a small control packet) which flood the network. There has been research conducted on reinforcement of the route in the forwards direction, reverse direction and both simultaneously. For backwards reinforcement a symmetric network is required and it couples the two directions together. In forward reinforcement, the route is rewarded before the outcome is known. Due to the fact that the system behaves stochastically and is hence lacks repeatability, there are some pretty significant obstacles to overcome for commercial deployment. Mobile media and new technologies could possibly change the threshold for collective action due to swarm intelligence.
Even airlines have made use of ant-based routing for the purpose of assigning aircraft arrivals to airport gates. Southwest Airlines makes use of a program that employs swarm theory or swarm intelligence working on the concept that a colony of ants works better than a single ant. Every pilot acts as an ant looking for the best airport gate. Through experience, the pilots would figure out what works best for themselves, and that could become the best solution for the airline. The “colony” of pilots would always go to gates they can arrive at and depart from quickly. The program can even inform pilots about plane back-ups before they happen.
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.
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.
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.
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.