Contextual Radio Cues for Enhancing Decision Making in Networks of Autonomous Agents

Introduction

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.

Objectives

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.

Methodology

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.

Conclusion

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.