Microbial intelligence (popularly known as bacterial intelligence) is the intelligence shown by microorganisms. The concept encompasses complex adaptive behaviour shown by single cells, and altruistic and/or cooperative behavior in populations of like or unlike cells mediated by chemical signalling that induces physiological or behavioral changes in cells and influences colony structures.
Complex cells, like protozoa or algae, show remarkable abilities to organise 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. These behaviors occur in single species populations, or mixed species populations. Examples are colonies of Myxobacteria, quorum sensing, and biofilms.
It has been suggested that a bacterial colony loosely mimics a biological neural network. The bacteria can take inputs in form of chemical signals, process them and then produce output chemicals to signal other bacteria in the colony.
The mechanisms that enable single celled organisms to coordinate in populations presumably carried over in those lines that evolved multicellularity, and were co-opted as mechanisms to coordinate multicellular organisms.
Artificial Robot Organisms – is a new research field within the domain of swarm, evolutionary and reconfigurable robotics. Some first works go to early 90s in the field of cellular robotics, lately it is performed by several Japan, American and European teams of researchers. Different aspects of this work are considered in several research projects, lately European Commission supported new research initiatives related to a new generation of artificial robot organisms.
Collective intelligence is often associated with macroscopic capabilities of coordination among robots, collective decision making, labor division and tasks allocation in the group. The main idea behind this is that robots are achieving better performance when working collectively and so are capable of performing such activities which are not possible for individual robots. The background of collective intelligence is related to the capability of swarm agents to interact jointly in one medium. There are three different cases of such interactions:
- In the first case agents communicate through a digital channel, capable for semantic messages exchange. Due to information exchange, agents build different types of common knowledge. This common knowledge in fact underlies collective intelligence.
- The second case appears when macroscopic capabilities are defined by environmental feedback. The system builds a closed macroscopic feedback-loop, which works in a collective way as a distributed control mechanisms. In this case there is no need of complex communication, agents interact only by kinetic means. This case if interaction is often denoted as a spatial reasoning, or spatial computing.
- The third case of interactions we encounter in nature, when some bacteria and fungi (e.g. dictyostelium discoideum) can aggregate into a multi-cellular organism when this provides better chances of survival. In this way, they interact not only through information exchange or spatial interactions, they build the closest physical, chemical and mechanical interconnections, through the agents still remain independent from each other. The first two cases of interactions are objects of extensive research in many domains: robotics, multi-agents systems, bio-inspired and adaptive community and so on. However, the practical research in the last case represents essential technological difficulties and therefore is not investigated enough. Despite the similarities between a robot swarm and multi-robot organism, such as a large number of robots, focus on collective/emergent behavior, a transition between them is a quite difficult step due to mechanical, electrical and, primarily, conceptual issues.
For More Information: Artificial Neural Networks
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