Collective Animal Intelligence
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- 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
- Decentralized: Federated & Distributed
- Distributed Deep Reinforcement Learning (DDRL)
- Agents' communication
- Bacterial Foraging Optimization Algorithm | Jason Brownlee
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:
- Fish:
- Fish School Search | Wikipedia
- Schools of fish swimming together; Cuttlefish Optimization Algorithm | Wikipedia
- Bat algorithm | Wikipedia
- Bees:
- Bees Algorithm | Wikipedia
- Termite mounds | Wikipedia
- Swarm intelligence (insects) | Wikipedia
- Flocking (birds) | Wikipedia
- Ant Colony Optimization Algorithms (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.
Swarm
- Robotics ... Vehicles ... Drones ... 3D Model ... Point Cloud
- Simulation ... Simulated Environment Learning ... World Models ... Minecraft: Voyager
- Swarm robotic platforms | Wikipedia
- Swarming (military) | Wikipedia
- Swarm Intelligence | Computational Thinking For Design
One of the most well-known examples of nature-inspired AI is swarm intelligence. Swarm intelligence is a type of collective behavior that emerges from the interactions of simple agents. Swarm intelligence is a rapidly growing field of research, and there are many new and exciting applications being developed all the time. As our understanding of swarm intelligence continues to grow, we can expect to see even more innovative and practical applications in the years to come. These agents are often autonomous and have limited capabilities, but they can achieve complex tasks by working together. Some examples of swarms in nature include flocks of birds, schools of fish, and colonies of ants.
The advantages of swarm intelligence include:
- It is scalable. Swarms can be made up of a large number of agents, which makes them well-suited for solving problems that are too complex for a single agent to solve.
- It is robust. Swarms can continue to function even if some of the agents are lost or damaged.
- It is adaptive. Swarms can learn and adapt to changes in their environment.
Some of the applications of swarm intelligence include:
Here are some specific examples of the advantages of swarm intelligence in real-world applications:
- In robotics, swarms of robots can be used to perform tasks that are too dangerous or difficult for humans, such as defusing bombs or exploring hazardous environments.
- In traffic control, swarm intelligence can be used to optimize traffic flow and reduce congestion. This can be done by using sensors to monitor traffic conditions and then adjusting the speed and direction of vehicles in real time.
- In financial trading, swarm intelligence can be used to make trading decisions and predict market trends. This can be done by using historical data to train a swarm of agents to identify patterns in the market.
- In data mining, swarm intelligence can be used to find patterns in large datasets. This can be done by using swarm agents to explore the dataset and identify clusters of data that are similar to each other.
