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CUE-GO

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Contextual Radio Cues for Enhancing Decision Making in Networks of Autonomous Ag...

Contextual Radio Cues for Enhancing Decision Making in Networks of Autonomous Agents The CUE-GO project aims to conceive a novel methodological framework for enhancing the decision-making capabilities of autonomous agents through the exploitation of contextual radio cues of the environment. Radio cues represent a... ver más

31/08/2029

CNR

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

CONSIGLIO NAZIONALE DELLE RICERCHE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-09-29

ERC-2023-STG: ERC STARTING GRANTS Scope:Objectives

I+D

Cerrada hace 2 años

0% 100% 100%

1 Participantes

CNR

1.06M€ | Lider

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Duración del proyecto: 71 meses Fecha Inicio: 2023-09-29
Fecha Fin: 2029-08-31

Líder del proyecto

CONSIGLIO NAZIONALE DELLE RICERCHE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 1M€

Descripción del proyecto The CUE-GO project aims to conceive a novel methodological framework for enhancing the decision-making capabilities of autonomous agents through the exploitation of contextual radio cues of the environment. Radio cues represent a quantum leap from the traditional concept of features, usually retrieved by vision-based systems, as they contain electromagnetic information with semantic meanings (contextual) enabled by radio-frequency sensors, like those at TeraHertz bands. The elaboration of contextual radio cues allows a far-reaching prediction of the outcomes of agents’ behaviors and ultimately yields a more efficient navigation in social environments, more accurate localization of people and objects, and enhanced cooperation toward a common mission goal. In interpreting contextual radio cues, agents exchange their sensed information in a way that considers other agents’ expertise, i.e., their abilities to process environmental stimuli. To achieve this vision, I will: (1) develop a general framework for the decision-making of autonomous agents that emulates the human capability of interpreting cues for anticipating an action’s course; (2) conceive and design methods for extrapolating contextual radio cues based on high-resolution semantic mapping of the environment; (3) conceive and design cue-guided localization and navigation algorithms that will boost ambient awareness; (4) conceive new methods and metrics to assess the agents’ skills in associating contextual radio cues with statistical models that accurately predict future rewards or punishments; (5) develop collaboration schemes accounting for the assessment of agents’ expertise. Thanks to a multidisciplinary approach, combining diverse knowledge from behavioral neuroscience to engineering, this project will lead to a significant advance in human-inspired decision-making for future networks of autonomous agents, toward a society where humans and artificial intelligence co-exist in the same environment.

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