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Controlling evolutionary dynamics of networked autonomous agents
Large-scale technological, biological, economic, and social complex systems act as complex networks of interacting autonomous agents. Large numbers of interacting agents making self-interested decisions can result in highly comple... Large-scale technological, biological, economic, and social complex systems act as complex networks of interacting autonomous agents. Large numbers of interacting agents making self-interested decisions can result in highly complex, sometimes surprising, and often suboptimal, collective behaviors. Empowered by recent breakthroughs in data-driven cognitive learning technologies, networked agents collectively give rise to evolutionary dynamics that cannot be easily modeled, analysed and/or controlled using current systems and control theory. Consequently, there is an urgent need to develop new theoretical foundations to tackle the emerging challenging control problems associated with evolutionary dynamics for networked autonomous agents. The aim of this project is to develop a rigorous theory for the control of evolutionary dynamics so that interacting autonomous agents can be guided to solve group tasks through the pursuit of individual goals in an evolutionary dynamical process. The theory will then be tested, validated and improved against experimental results using robotic fish. To achieve the aim, I will: (1) develop a general formulation for stochastic evolutionary dynamics with control inputs, enabling the study on controllability and stabilizability for evolutionary processes; (2) introduce stochastic control Lyapunov functions to design control laws; (3) construct new classes of conditional strategies that may propagate controlled actions effectively from focal agents in multiple time scales; and (4) validate experimentally on tasks with unknown difficulties that require a group of robotic fish to evolve and adapt. The project will result in a major advance from the conventional usage of evolutionary game theory with the systematic design to actively control evolutionary outcomes. The combination of theory with experimentation and the multi-disciplinary nature of the approach will lead to new applications of autonomous robotic systems. ver más
30/04/2023
RIJKSUNIVERSITEIT...
2M€
Perfil tecnológico estimado
Duración del proyecto: 61 meses Fecha Inicio: 2018-03-13
Fecha Fin: 2023-04-30

Línea de financiación: concedida

El organismo H2020 notifico la concesión del proyecto el día 2023-04-30
Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:
ERC-2017-COG: ERC Consolidator Grant
Cerrada hace 7 años
Presupuesto El presupuesto total del proyecto asciende a 2M€
Líder del proyecto
RIJKSUNIVERSITEIT GRONINGEN No se ha especificado una descripción o un objeto social para esta compañía.
Perfil tecnológico TRL 4-5