Swarm Intelligence
Swarm Intelligence (SI) is a kind of artificial intelligence that aims to simulate the behavior of swarms or social insects. Swarm refers to any loosely structured collection of interacting agents. Technically swarms are regarded as decentralized self-organized systems. Swarm intelligence has a multidisciplinary character. Its study provides insights that can help humans manage complex systems. There is no clear definition for swarm intelligence. Emergent behaviour, self-organized behaviour and collective intelligence are the related terms. Surprisingly swarm intelligence system has the ability to act in a coordinated way without any coordinator or external controller.
Swarm Intelligence of Ants
Collective intelligence is the key. A single ant, for example, is not that smart but a colony of ants is. As colonies, ants respond quickly and effectively to their environment. They find shorted path to the best food source, allocate workers to different tasks, and defend their territory from enemies. Ant colonies make these possible by countless interactions between individual ants. Each ant follows a simple rule of thumb. Each ant acts only on local information. A system that exhibits this behavior is said to be self-organizing. And the intelligence that the ants exhibit collectively is called swarm intelligence.
Marco Dorigo, at the Université Libre in Brussels, used swarm intelligence in 1991 to create mathematical procedures for solving complex problems, such as routing trucks, scheduling airlines, or guiding military robots.
Swarm Intelligence of Honey Bees
Honey bees also exhibit swarm intelligence. Thomas Seeley, a biologist at Cornell University, has found the ability of honeybees to make good decisions. With as many as 50,000 workers in a single hive, honeybees have evolved ways to work to do what’s best for the colony. Honey bees use an odor for conveying information. Honeybee scouts waggle dance to report on food. They also dance to report on real estate. The dance will be stronger for better real estate.
Applications of Swarm Intelligence
Beckers et al. (1994) have programmed a group of robots to implement clustering behavior of ants. This is one of the first swarm intelligence scientific oriented studies in which artificial agents were used.
A number of swarm intelligence studies have been performed with swarms of robots for validating mathematical models of biological systems.
In a classic experiment done in 1990, Deneubourg and his group showed that, when given the choice between two paths of different length joining the nest to a food source, a colony of ants has a high probability to collectively choose the shorter one. Deneubourg has shown that this behavior can be explained via a simple probabilistic model in which each ant decides where to go by taking random decisions based on the intensity of pheromone perceived on the ground, the pheromone being deposited by the ants while moving from the nest to the food source and back.
Turing Machines
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During the 1930s-1950s many researchers debated over what was computable, and what wasn't. Many had argued over formal approaches to computability. In 1937, Alan Turing, a British mathematician who is now considered the father of computing and artificial intelligence sought to seek an answer to this dilemna. He constructed the theory of a Turing machine. His theorem (the Church-Turing thesis) states that
Any effective procedure (or algorithm) can be implemented through a Turing machine.
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So what are Turing machines? Turing machines are abstract mathematical entities that are composed of a tape, a read-write head, and a finite-state machine. The head can either read or write symbols onto the tape, basically an input-output device. The head can change its position, by either moving left or right. The finite state machine is a memory/central processor that keeps track of which of finitely many states it is currently in. By knowing which state it is currently in, the finite state machine can determine which state to change to next, what symbol to write onto the tape, and which direction the head should move (left or right). (Note: the tape shall be assumed to be as large as is neccessary for the current computation it was assigned) As seen in the above figure, input onto the tape comprises of some finite alphabet (in this case it consists of 0, 1, blank). Thus, the Turing machine can do three possible things.
It can write a new symbol at its current position on the tape.
It can assume a new state.
it can move the position of the head one position to either the left or the right.
This machine is (by the Church-Turing thesis) capable of making any computation. This is not a provable theorem (it has yet to be disproved) nor a strictly formal definitive approach, the Church-Turing thesis is based on our intuition of what computation is about. By understanding what Turing machines can compute, we can also gain a better grasp of the potential of production systems for computing.