"Using primitive brains to achieve emergent smart solutions"

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Springer Cham
Either for or against the validity of Kurzweil's law, it is a fact that technology accelerates at an astonishing pace achieving breathtaking results in any kind of human activity. The Internet of Things, the Cloud, Fog and Edge computing, the daily increasing visions for smarter systems following the advancements in machine learning, and many more technological innovations lead to more demanding requirements than in previous decades for emergent applications of extreme complexity. A promising solution to deal with such complexity is to employ systems that exhibit self properties, composed by simple agents that communicate and interact following simple protocols achieving desirable emergent properties that allow smart solutions in dynamic environments of extreme complexity. Nature through millions of years of evolution has many systems like that to exhibit. Studying systems of agents with primitive brains that demonstrate remarkable self properties that emerge and are not explicitly engineered could prove of great value regardless of the required effort. Imitating similar behaviors in artificial systems could offer smart solutions to problems exhibiting high-level complexity that seemed unsolvable, or are solved under very restricting and concrete conditions. This presentation will present and discuss experiences studying ants, largebodied animals, bees, hornets, focusing on the latest study of frogs and how their mating strategies could potentially lead to smart solutions in acoustic scene analysis field, disaster management, and many other complex dynamic systems.