A Comparative Analysis of AI Chatbot Performance in IoT Environments

Abstract

This research undertakes a comprehensive comparative analysis of AI chatbots to identify strengths, weaknesses, and potential areas for improvement. By scrutinizing key performance metrics such as natural language understanding, response generation, dialogue management, and task completion, this study aims to contribute to the comparison of chatbot technology. A rigorous evaluation of existing chatbots will provide valuable insights into the underlying algorithms, architectures, and training data that influence their performance. Furthermore, by benchmarking chatbots across diverse domains and applications, this research seeks to establish a deep learning approach for assessing chatbot capabilities. The findings of this study will inform the development of more sophisticated and effective chatbots, benefiting both researchers and industry practitioners. Ultimately, this research contributes to the broader field of computer science by advancing the state-of-the-art in natural language processing, machine learning, and human-computer interaction. Conversational agents (CAs), or chatbots, powered by Artificial Intelligence (AI), have emerged as a promising solution. However, selecting the optimal chatbot platform for a specific connected environment can be challenging. This paper proposes a novel approach utilizing Machine Learning (ML) techniques to compare and analyze functionalities and user experience (UX) of leading AI chatbot platforms. By leveraging user reviews, technical specifications, and user testing data, our ML-driven framework will rank and categorize chatbot platforms based on pre-defined criteria, empowering users to make informed decisions for their specific IoT needs.