DiODe
Financiado
Distributed Algorithms for Optimal Decision Making
This grant will develop and translate a unifying framework for optimal decision-theory, and observations of natural systems, to design distributed algorithms for decentralised decision-making. This will enable a technological step...
This grant will develop and translate a unifying framework for optimal decision-theory, and observations of natural systems, to design distributed algorithms for decentralised decision-making. This will enable a technological step-change in techniques for controlling distributed systems, primarily demonstrated during the grant by decentralised control of robot swarms. These algorithms and associated methodology will also provide hypotheses and tools to change the way scientists think about and interrogate natural decision mechanisms, from intracellular regulatory networks, via neural decision circuits, to decision-making populations of animals. Specific objectives are:
1. Distributed value-sensitive decision-making: undertake optimality analyses of the applicant’s existing decentralised decision-making algorithms based on observations of collective iterated voting-processes in honeybees, and extend these.
2. Distributed sampling and decision-making: design distributed mechanisms that implement optimal compromises between sampling information and making decisions based on that information.
3. Individual-confidence and distributed decision-making: translate machine learning theory to collective behaviour models, designing mechanisms in which weak decision-makers optimally combine their decisions to optimise group performance.
4. Optimal distributed decision-making in collective robotics: translate theory from objective 1 to 3 towards practical applications in artificial systems, demonstrated using collectively-deciding robots.
5. Development of tools for life scientists and validation of theoretical predictions in natural systems: interact with named collaborators to investigate identified decision mechanisms in single cells, in neural circuits, and in social groups. Develop accessible modelling tools to facilitate investigations by life scientists.
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Duración del proyecto: 67 meses
Fecha Inicio: 2015-04-28
Fecha Fin: 2020-11-30
Fecha Fin: 2020-11-30
Línea de financiación: concedida
El organismo H2020 notifico la concesión del proyecto el día 2020-11-30
Línea de financiación objetivo
El proyecto se financió a través de la siguiente ayuda:
ERC-CoG-2014:
ERC Consolidator Grant
Cerrada
hace 10 años
Presupuesto
El presupuesto total del proyecto asciende a
1M€
Líder del proyecto
THE UNIVERSITY OF SHEFFIELD
No se ha especificado una descripción o un objeto social para esta compañía.
Perfil tecnológico
TRL
4-5
Perfil tecnológico
TRL
4-5
529.60K€ |
Participante