March 7, 2015
Microbial intelligence is the intelligence shown by microorganisms, including complex adaptive behavior shown by single cells, and altruistic and/or cooperative behavior in populations of like or unlike cells. While the number of microorganisms in a colony can run into the billions, each individual is able to stay synchronized by sharing simple chemical messages. Complex cells, like protozoa or algae, show remarkable abilities to organize themselves in changing circumstances. Shell-building by amoebae, reveals complex discrimination and manipulative skills that are ordinarily thought to occur only in multicellular organisms.
Even bacteria, which show primitive behavior as isolated cells, can display more sophisticated behavior as a population. Examples include: myxobacteria (which form motile slime colonies), quorum sensing (a system of stimulus and response correlated to population density), and biofilms (cells that cooperate to stick to each other on a surface). It has been suggested that a bacterial colony loosely mimics a biological neural network (i.e. a brain). The bacteria can take inputs in form of chemical signals, process them and then produce output chemicals to signal other bacteria in the colony.
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March 7, 2015
Swarm Intelligence (SI) refers to numerous, simple units working in concert to solve complex problems. It is field in computer science and artificial intelligence based on examples from nature such as an ant colony, made of many animals that communicate with each other to achieve unified goals. In computer models the ‘animals,’ or individual units, are called ‘agents.’ Swarm intelligence emerges from decentralized, self-organizing systems, natural or artificial. The expression was introduced by electrical engineers Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems. The application of swarm principles to robots is called ‘swarm robotics,’ while ‘swarm intelligence’ refers to the more general set of algorithms. ‘Swarm prediction’ has been used in the context of forecasting problems.
SI systems consist typically of a population of simple agents or ‘boids’ (named for a 1986 artificial life program that simulates the flocking behavior of birds) interacting locally with one another and with their environment. A number of natural systems have been used as models (e.g. animal herding, bacterial growth, fish schooling and microbial intelligence). The agents follow very simple rules, and although there is no centralized control structure dictating how each unit should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of ‘intelligent’ global behavior, unknown to the individuals.
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