SwarmChat brings together the power of large language models and behavior trees to revolutionize how we command robotic swarms. By combining LLM-driven natural language understanding with the structured decision-making of BTs, SwarmChat lets users issue high-level, conversational instructions that are automatically translated into coordinated swarm behaviors. Beyond text, the interface also handles audio input—transcribing speech and interpreting commands in real time—so humans can interact with complex multi-robot systems as easily as having a conversation.

This project focuses on integrating AI agents with advanced matching algorithms to create EnhanceMatch, a system designed to optimize recruitment. By efficiently matching candidates’ CVs with job applications, EnhanceMatch streamlines the hiring process, bridging the gap between job seekers and employers. The research aims to automate and enhance the recruitment process, making it more efficient and effective.