Researchers Julian Jeggle and Raphael Wittkowski, affiliated with the Max Planck Institute for Intelligent Systems in Stuttgart, Germany, have explored the potential of creating intelligent systems from simple, motile agents in a recent book chapter. Their work delves into the concept of “intelligent matter” and how it can be applied to solve complex problems in the energy sector and beyond.
The researchers begin by highlighting a well-known phenomenon in nature: large groups of entities following simple rules, such as swarms of animals, can exhibit complex collective behavior. This emergent behavior raises the question of whether synthetic matter can be designed to mimic this complexity and achieve intelligent systems. Jeggle and Wittkowski define “intelligent matter” and compare it to recent advancements in the field of active matter, which deals with systems of self-propelled particles.
One approach they explore is emergent computing, where specialized active matter systems are designed to solve specific tasks through emergent behavior. For instance, in the energy sector, this could potentially lead to the development of smart grids that can self-regulate and optimize energy distribution based on real-time demand and supply.
The researchers also discuss physical reservoir computing, which leverages the dynamics of active particle systems to process information. They introduce a novel reservoir computing scheme for active particles driven by ultrasound or light refraction. This technology could have practical applications in energy storage systems, where it could help manage and optimize the charging and discharging processes of batteries.
Jeggle and Wittkowski’s work provides a theoretical framework for creating intelligent systems from simple, motile agents. While the research is still in its early stages, it offers promising avenues for the energy sector to explore, particularly in the areas of smart grids and energy storage. The book chapter was published in the volume “Emergent Intelligence of Self-Organized Nonlinear Systems” by Springer.
This article is based on research available at arXiv.