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| − | Group E | + | Swarm Intelligence --- Group E |
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| + | Swarm intelligence involves a complex system of semi-autonomous individual units working collectively to reach an optimum solution through environmental and intra-population interactions . This cooperation causes comprehensive patterns to emerge across the system through which the system can be better understood and researched. | ||
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| + | Common examples of swarm intelligence systems include bee hives, ant colonies, human crowds, evolution, and bird flocking. Often, these systems are modeled using algorithms and then applied to solve specific problems. | ||
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| + | The discipline of swarm intelligence was pioneered in 1989 by the research of Gerardo Beni and Jing Wang, which focused on cellular swarm robotics. | ||
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| + | Concepts | ||
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| + | Collectivity (Decentralization): the population consists of many individuals without a centralized source of control. | ||
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| + | Uniformity (Homogeny): individual units of the population are either undifferentiated or largely similar with specialized differences. | ||
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| + | Self-organization (Stigmergy): individuals act and interact on the local level based on genetic instinct and learned behaviors, leading to a macrocosmic pattern becoming evident and individual intelligence becoming collectively magnified. | ||
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| + | Classifications | ||
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| + | Natural or artificial—swarm intelligence involving organic networks existing in nature is called “natural”, while swarm intelligence involving man-made systems is called “artificial”. | ||
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| + | Scientific or engineering—research aiming to understand and replicate swarm intelligence systems is referred to as “scientific”, while utilization of swarm intelligence networks in search of a solution to a specific problem is described as “engineering”. | ||
[[Category:MA279Spring2016Walther]] | [[Category:MA279Spring2016Walther]] | ||
Revision as of 14:36, 22 April 2016
Swarm Intelligence --- Group E
Swarm intelligence involves a complex system of semi-autonomous individual units working collectively to reach an optimum solution through environmental and intra-population interactions . This cooperation causes comprehensive patterns to emerge across the system through which the system can be better understood and researched.
Common examples of swarm intelligence systems include bee hives, ant colonies, human crowds, evolution, and bird flocking. Often, these systems are modeled using algorithms and then applied to solve specific problems.
The discipline of swarm intelligence was pioneered in 1989 by the research of Gerardo Beni and Jing Wang, which focused on cellular swarm robotics.
Concepts
Collectivity (Decentralization): the population consists of many individuals without a centralized source of control.
Uniformity (Homogeny): individual units of the population are either undifferentiated or largely similar with specialized differences.
Self-organization (Stigmergy): individuals act and interact on the local level based on genetic instinct and learned behaviors, leading to a macrocosmic pattern becoming evident and individual intelligence becoming collectively magnified.
Classifications
Natural or artificial—swarm intelligence involving organic networks existing in nature is called “natural”, while swarm intelligence involving man-made systems is called “artificial”.
Scientific or engineering—research aiming to understand and replicate swarm intelligence systems is referred to as “scientific”, while utilization of swarm intelligence networks in search of a solution to a specific problem is described as “engineering”.
