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Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Jing Wang and Gerardo Beni in 1989, in the context of cellular robotic systems.
The analysis highlights Applications, Research, Art and Products as prominent areas in the source structure around Swarm intelligence.
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Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
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The extracted context around Swarm intelligence shows recurring relationship patterns in the source. For example, Swarm intelligence → Aber, Ant-based, Anthony Lewis, Bekey, Conversely, George, IBN, Intent-Based Networking, Internet, IoT, NASA, Rifaie, SI, Swarm, Swarm Intelligence-based, The European Space Agency, Things Another extracted example is Swarm intelligence → Agriculture Organization, Artificial Swarm Intelligence, ASI, FAO, Food, Human Swarming, Sometimes, Swarm AI, United Nations, Vegas. Use these groups to spot repeated connection types before inspecting the individual relationships.
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swarm intelligence systems used algorithms artificial optimization also one control boids ant social potential swarming robots using collective agents force
TTTA extracted 71 structured relationships around Swarm intelligence. Examples in this analysis include assembling → instance of → The 1999 paper envisioned many industrial and military applications and simulated annealing is that the large number of members that make up the particle swarm make the technique impressively resilient to the problem of local minima.Artificial Swarm Intelligence → instance of → The main advantage of such an approach over other global minimization strategies. The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| assembling | instance of | The 1999 paper envisioned many industrial and military applications | 0.80 | text |
| transporting | instance of | The 1999 paper envisioned many industrial and military applications | 0.80 | text |
| hazardous inspection | instance of | The 1999 paper envisioned many industrial and military applications | 0.80 | text |
| patrolling | instance of | The 1999 paper envisioned many industrial and military applications | 0.80 | text |
| and military control of swarm systems | instance of | The 1999 paper envisioned many industrial and military applications | 0.80 | text |
| simulated annealing is that the large number of members that make up the particle swarm make the technique impressively resilient to the problem of local minima.Artificial Swarm Intelligence | instance of | The main advantage of such an approach over other global minimization strategies | 0.80 | text |
| simulated annealing is that the large number of members that make up the particle swarm make the technique impressively resilient to the problem of local minima | instance of | The main advantage of such an approach over other global minimization strategies | 0.80 | text |
| crowd movement or flocking.Ant-based routingThe use of swarm intelligence in telecommunication networks has also been researched | instance of | and in gaming and simulations to create realistic group behaviors | 0.80 | text |
| in the form of ant-based routing | instance of | and in gaming and simulations to create realistic group behaviors | 0.80 | text |
| found in art | instance of | technology compared to majority voting.Swarm grammarsSwarm grammars are swarms of stochastic grammars that can be evolved to describe complex properties | 0.80 | text |
| architecture | instance of | technology compared to majority voting.Swarm grammarsSwarm grammars are swarms of stochastic grammars that can be evolved to describe complex properties | 0.80 | text |
| found in art | instance of | Swarm grammarsSwarm grammars are swarms of stochastic grammars that can be evolved to describe complex properties | 0.80 | text |
The concept neighborhoods around Swarm intelligence bring nearby vocabulary together. In this analysis, examples include Swarm, Collective and Systems. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
For Swarm intelligence, one of the stronger structural bridges in this analysis connects Swarm intelligence with Overview. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
TTTA analyzes the structure around Swarm intelligence to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Applications, Research, Art & Products, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — Swarm intelligence · EN edition · Analysis: TopicsToTalkAbout
