A Sketch of DSL to Accelerate the Development of Reactive Chatbots in Safe Transportation

Abstract

Chatbots are tools designed to interact with users through natural language. They are widely used in various sectors, such as education, tourism, and transportation. These systems perform several common tasks, such as enhancing customer service, providing information permanently, and answering frequently asked questions. They can be classified into two main categories: rule-based chatbots like Eliza, which rely on predefined rules and intents to handle specific tasks, and AIbased chatbots like ChatGPT, which use advanced technologies like deep learning and Natural Language Processing (NLP) to interpret and respond to user inputs dynamically. However, their development faces challenges due to constraints specific to their development tools, such as high-cost NLP services. In addition, the absence of a dedicated chatbot development platform for the transportation domain remains a significant limitation. To bridge this gap, this study conducts a comparative analysis of existing metamodels for chatbot development and identifies their concepts and relations. The outcome is the design of a unified metamodel specifically tailored to the transportation sector, serving as the abstract syntax for constructing a DomainSpecific Language (DSL) that accelerates chatbot development for the transportation domain and reduces costs associated with NLP services.