Distributed Artificial Intelligence (DAI) manages information and actions in multi-agent systems, exemplified by swarm exploration. Agents collaborate for optimal learning, intelligent information gathering, and coordinated movement.

This project investigates novel methods of semantic communication to process and transmit measured sensor data from the exploration. In contrast to classical Shannon communication, semantic communication not only emphasizes reliable sensor data transmission but also utilizes machine learning techniques to design efficient transmitters and receivers for relevant exploration information.

Furthermore, the goal is to develop a framework, methodology, and algorithms for a "tight" integration of exploration and communication. Through probabilistic learning and model-based AI, the exploration is intended to become "communication-aware," and the communication "exploration-aware."

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