Augmenting Automation: Intent-Based User Instruction Classification with Machine Learning

arXiv:2403.01242v1 Announce Type: new
Abstract: 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 …

abstract adaptability arxiv automation classification control cs.ai cs.cl cs.hc cs.lg devices efficiency electric flexibility machine machine learning novel paper systems type