VISION

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 radiofrequency 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 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.

OBJECTIVES

Obj. 1: A human-inspired framework for heterogeneous networks of autonomous agents. 

Obj. 2: High-resolution radio mapping and extrapolation of contextual radio cues. 

Obj. 3: Cooperation techniques and self-assessment of proficiency. 

Obj. 4: Contextual cue-guided localization, tracking, and autonomous navigation.