Researchers at ETH Zurich and the Frankfurt School have developed an artificial neural network that can solve challenging control problems. The self-learning system can be used for the optimization of supply chains and production processes as well as for smart grids or traffic control systems.
Power cuts, financial network failures and supply chain disruptions are just some of the many of problems typically encountered in complex systems that are very difficult or even impossible to control using existing methods. Control systems based on artificial intelligence (AI) can help to optimize complex processes—and can also be used to develop new business models.
Together with Professor Lucas Böttcher from the Frankfurt School of Finance and Management, ETH researchers Nino Antulov-Fantulin and Thomas Asikis—both from the Chair of Computational Social Science—have developed a versatile AI-based control system called AI Pontryagin which is designed to steer complex systems and networks towards desired target states. Using a combination of numerical and analytical methods, the researchers demonstrate how AI Pontryagin automatically learns to control systems in near-optimal ways even when the AI has not previously been informed of the ideal solution.
Continue reading: https://techxplore.com/news/2022-01-complex-artificial-intelligence.html
Power cuts, financial network failures and supply chain disruptions are just some of the many of problems typically encountered in complex systems that are very difficult or even impossible to control using existing methods. Control systems based on artificial intelligence (AI) can help to optimize complex processes—and can also be used to develop new business models.
Together with Professor Lucas Böttcher from the Frankfurt School of Finance and Management, ETH researchers Nino Antulov-Fantulin and Thomas Asikis—both from the Chair of Computational Social Science—have developed a versatile AI-based control system called AI Pontryagin which is designed to steer complex systems and networks towards desired target states. Using a combination of numerical and analytical methods, the researchers demonstrate how AI Pontryagin automatically learns to control systems in near-optimal ways even when the AI has not previously been informed of the ideal solution.
Continue reading: https://techxplore.com/news/2022-01-complex-artificial-intelligence.html
