Cognitive algorithm in Social Insects summarized by five behavioral principles

Various companies and academic institutions are actively researching the field of swarm intelligence. A search on the topic reveals two distinct approaches:

a)  Each member is controlled through a central computer, e.g. Intel’s 100 drones.

b)  Each member behaves autonomously without a central computer, e.g. Harvard University’s 1024 Robot Swarm.

Both approaches have merits and limitations.

In the case of “a” above, members are slaves in a system controlled by a central computer with sufficient channels of communication. The results can be visually spectacular, as illustrated by Intel’s drones. However, since a central computer dictates the movement of each member, there is limited flexibility to adapt to changing environments, such as: x) members lost to unforeseen events, y) members added to speed up the mission, or z) members autonomously self-allocating labor.

Of course, the intricacy of the central software may be increased to account for x, y, and z, but that would make the central computer responsible for real-time response, increase vulnerability due to single point of failure, and it would deviate from the concept of swarm intelligence which is defined as the collective behavior of decentralized, self-organized systems.

In the case of "b,"members have the autonomy to adapt without a central dictator. Considering that each member possesses modest processing power — as illustrated by Harvard University’s swarm of robots — the results are truly impressive. Nevertheless, this type of behavior falls into the realm of flocking. It does meet swarm intelligence’s basic definition of collective behavior of decentralized, self-organized systems, but it still lacks the most important attribute of social insects, i.e. the ability to autonomously distribute and undertake allocation of labor.

If neither “a” nor “b” meets the requirements of autonomous allocation of labor, then we need a different approach:

Let’s start by defining the rules of the game: the cognitive algorithm should be compliant with the five principles exhibited by the experts in true swarm intelligence, i.e., social insects.

Principle #1: Awareness

Each member must be aware of its surroundings and abilities.

Principle #2: Autonomy

Each member must operate as an autonomous master (not as a slave;) this is essential to self-coordinate allocation of labor.

Principle #3: Solidarity

Each member must cooperate in solidarity: when a task is completed, each member should autonomously look for a new task (leveraging its current position.)

Principle #4: Expandability

The system must permit expansion where members are dynamically aggregated.

Principle #5: Resiliency

The system must be self-healing: when members are removed, the remaining members should undertake the unfinished tasks.

The video below is a proof-of-concept that demonstrates the five principles of swarm intelligence — the prototype is based on the Solidarity Cell Architecture.