Descripción del proyecto
Nuevos algoritmos de control de sistemas distribuidos
En la actualidad, se carece de marcos óptimos para la toma de decisiones que permitan controlar eficazmente grandes grupos de agentes autónomos, como los enjambres de robots, sin un control centralizado. En este contexto, el equipo del proyecto DiODe, financiado por el Consejo Europeo de Investigación, creará algoritmos para controlar sistemas distribuidos, centrándose principalmente en el control descentralizado de enjambres de robots. La investigación también proporcionará herramientas para investigar los mecanismos naturales de toma de decisiones en los organismos vivos, desde las redes intracelulares hasta las poblaciones de animales que toman decisiones. En el proyecto se abordarán varios objetivos, entre ellos el diseño de mecanismos distribuidos para aplicar compromisos óptimos entre el muestreo de información y la toma de decisiones. También se traducirá la teoría en aplicaciones prácticas para sistemas artificiales y se desarrollarán herramientas de modelización accesibles para los investigadores en ciencias de la vida.
Objetivo
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.
Ámbito científico
- natural sciencesbiological sciencesecology
- natural sciencesbiological scienceszoologyentomologyapidology
- natural sciencesbiological sciencesneurobiologycomputational neuroscience
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- natural sciencescomputer and information sciencessoftwaresoftware applicationssimulation software
Programa(s)
Régimen de financiación
ERC-COG - Consolidator GrantInstitución de acogida
S10 2TN Sheffield
Reino Unido