Adaptive and Scalable Knowledge Management for Robotic Applications via Logic Languages

This work outlines a framework that combines knowledge representation systems with planning capabilities. It is based on four main pillars: Automated knowledge extraction from natural language descriptions using Large Language Models (LLMs). Generation of temporal plans for multi-robot systems using inference rules. Automatic translation of plans into executable formalisms. Runtime monitoring of the plan execution, learning from exceptions, and updating the knowledge-base dynamically.