Combining logical reasoning and LLMs toward creating multi-agent smart home systems

The rapid advancement of AI technologies, particularly Large Language Models (LLMs), has sparked interest in their integration into Multi-Agent Systems (MAS). This holds substantial promise for applications such as smart homes, where it can significantly enhance user experience by optimizing comfort, energy efficiency, and security. Despite the potential benefits, the implementation of MAS based on LLMs faces several challenges, including the risks of hallucinations, scalability issues, and concerns about the reliability of these systems in real-world applications. This study explores the development of MAS incorporating LLMs, with a focus on mitigating hallucinations through the integration of formal logical models for knowledge representation and decision-making, along with other machine learning methods. To demonstrate the efficacy of this approach, we conducted experiments with a plant care module within a smart home system. The results show that our approach can significantly reduce hallucinations and enhance the overall reliability of the system. Further research will focus on refining these methods to enhance adaptability and scalability to ensure system’s functionality in real-world environments.