Swarm intelligence

A flock of starlings reacting to a predator

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 Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.[1][2]

SI systems consist typically of a population of simple agents or boids interacting locally with one another and with their environment.[3] The inspiration often comes from nature, especially biological systems.[4] The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of "intelligent" global behavior, unknown to the individual agents.[5] Examples of swarm intelligence in natural systems include ant colonies, bee colonies, bird flocking, hawks hunting, animal herding, bacterial growth, fish schooling and microbial intelligence.

The application of swarm principles to robots is called swarm robotics while swarm intelligence refers to the more general set of algorithms. Swarm prediction has been used in the context of forecasting problems. Similar approaches to those proposed for swarm robotics are considered for genetically modified organisms in synthetic collective intelligence.[6]

  1. ^ Beni, G.; Wang, J. (1993). "Swarm Intelligence in Cellular Robotic Systems". Proceed. NATO Advanced Workshop on Robots and Biological Systems, Tuscany, Italy, June 26–30 (1989). Berlin, Heidelberg: Springer. pp. 703–712. doi:10.1007/978-3-642-58069-7_38. ISBN 978-3-642-63461-1.
  2. ^ Beni, G. (1989). "The concept of cellular robotic system". Proceedings IEEE International Symposium on Intelligent Control 1988. IEEE. pp. 57–62. doi:10.1109/ISIC.1988.65405. ISBN 978-0-8186-2012-6.
  3. ^ Hu, J.; Turgut, A.; Krajnik, T.; Lennox, B.; Arvin, F., "Occlusion-Based Coordination Protocol Design for Autonomous Robotic Shepherding Tasks" IEEE Transactions on Cognitive and Developmental Systems, 2020.
  4. ^ Gad, Ahmed G. (2022-08-01). "Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review". Archives of Computational Methods in Engineering. 29 (5): 2531–2561. doi:10.1007/s11831-021-09694-4. ISSN 1886-1784.
  5. ^ Hu, J.; Bhowmick, P.; Jang, I.; Arvin, F.; Lanzon, A., "A Decentralized Cluster Formation Containment Framework for Multirobot Systems" IEEE Transactions on Robotics, 2021.
  6. ^ Solé R, Rodriguez-Amor D, Duran-Nebreda S, Conde-Pueyo N, Carbonell-Ballestero M, Montañez R (October 2016). "Synthetic Collective Intelligence". BioSystems. 148: 47–61. Bibcode:2016BiSys.148...47S. doi:10.1016/j.biosystems.2016.01.002. hdl:10630/32279. PMID 26868302.