Augmenting Automation: Intent-Based User Instruction Classification with Machine Learning
This work addresses the need for more intuitive and adaptable control in electric automation systems, representing an incremental improvement over traditional methods.
The paper tackles the problem of inflexibility in electric automation systems by proposing an intent-based user instruction classification approach using machine learning, which enables dynamic control without predefined commands and enhances user experience.
Electric automation systems offer convenience and efficiency in controlling electrical circuits and devices. Traditionally, these systems rely on predefined commands for control, limiting flexibility and adaptability. In this paper, we propose a novel approach to augment automation by introducing intent-based user instruction classification using machine learning techniques. Our system represents user instructions as intents, allowing for dynamic control of electrical circuits without relying on predefined commands. Through a machine learning model trained on a labeled dataset of user instructions, our system classifies intents from user input, enabling a more intuitive and adaptable control scheme. We present the design and implementation of our intent-based electric automation system, detailing the development of the machine learning model for intent classification. Experimental results demonstrate the effectiveness of our approach in enhancing user experience and expanding the capabilities of electric automation systems. Our work contributes to the advancement of smart technologies by providing a more seamless interaction between users and their environments.