Difference between revisions of "Collective Animal Intelligence"
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| − | * Schools of fish swimming together | + | * Schools of fish swimming together; [https://en.wikipedia.org/wiki/User_talk:Cuttlefish_Optimization_Algorithm Cuttlefish Optimization Algorithm | Wikipedia] |
| − | * Bees | + | * [https://en.wikipedia.org/wiki/Bees_algorithm Bees Algorithm | Wikipedia] |
* Termite mounds | * Termite mounds | ||
* [https://en.wikipedia.org/wiki/Flocking_(behavior) Flocking (birds) | Wikipedia] | * [https://en.wikipedia.org/wiki/Flocking_(behavior) Flocking (birds) | Wikipedia] | ||
Revision as of 19:38, 19 August 2023
Youtube search... ...Google search
- Collective Animal Intelligence ... Animal Ecology ... Animal Language ... Bird Identification
- Symbiotic Intelligence ... Bio-inspired Computing ... Neuroscience ... Connecting Brains ... Nanobots ... Molecular ... Neuromorphic ... Evolutionary/Genetic
- Cybersecurity ... OSINT ... Frameworks ... References ... Offense ... NIST ... DHS ... Screening ... Law Enforcement ... Government ... Defense ... Lifecycle Integration ... Products ... Evaluating
- Sakana ... inspired by the way that fish and other animals work together in groups
One of the most well-known examples of Bio-inspired Computing is Collective Animal Intelligence (CAI). CAI is the study of how groups of animals work together to achieve a common goal. Some examples of CAI include:
- Schools of fish swimming together; Cuttlefish Optimization Algorithm | Wikipedia
- Bees Algorithm | Wikipedia
- Termite mounds
- Flocking (birds) | Wikipedia
- Ant Colony Optimization Algorithms (insects) | Wikipedia
- Swarm intelligence (insects) | Wikipedia
- Particle Swarm Optimization | Wikipedia
- Shoaling & Schooling (fish) | Wikipedia
- Herd Behavior (land animals) | Wikipedia
These groups of animals are able to achieve complex tasks by working together in a coordinated way. They do this without any central planning or communication. Instead, they rely on simple rules of behavior that emerge from the interactions of the individual animals. Bio-inspired AI researchers are interested in understanding how these simple rules can lead to complex and intelligent behavior. They believe that by studying CAI, they can develop new AI algorithms that are more efficient, robust, and adaptive than traditional AI algorithms.
