Current chatbot development platforms and frameworks facilitate setting up the language and dialog part of chatbots, while connecting it to backend services and business functions requires substantial manual coding effort and programming skills. This paper proposes an approach to overcome this situation. It proposes an architecture with a chatbot as frontend using an IoT (Internet of Things) platform as a middleware for connections to backend services. Specifically, it elaborates and demonstrates how to combine a chatbot developed on the open source development platform Rasa with the open source platform Node-RED, allowing low-code or no-code development of a transactional conversational user interface from frontend to backend. This is the author's version of the accepted version of the paper. It is posted here for your personal use. The final published version of the paper is in CUI'21 3rd Conference on Conversational User Interfaces, July 27-29, 2021, Bilbao (online), Spain. It can be accessed at https://doi.org/10.1145/3469595.3469632 1 INTRODUCTION Chatbots offer a means to use services and control smart home devices connected to the web.

Rasa is an open-source conversational AI framework that uses machine learning to build chatbots and AI assistants. Today, I'm going to show you how to build your own simple chatbot using Rasa and deploying it as a bot to Facebook messenger -- all within an hour. All you need is some simple Python programming and a working internet connection. The code was developed and tested in Python 3.7. Rasa currently only supports Python up to 3.8 (see here for updates).

In this article, I will share my workflow for creating conversational AI agents using the Rasa framework. It uses VS Code and Docker to automate repetitive tasks and code in an OS-agnostic way. The code to reproduce the results can be found here. I assume the reader has a basic familiarity with Rasa and Docker. The goal of this workflow is to make it easy for developers to alternate between these 3 tasks.

Smart speakers and robots become ever more prevalent in our daily lives. These agents are able to execute a wide range of tasks and actions and, therefore, need systems to control their execution. Current state-of-the-art such as (deep) reinforcement learning, however, requires vast amounts of data for training which is often hard to come by when interacting with humans. To overcome this issue, most systems still rely on Finite State Machines. We introduce Petri Net Machines which present a formal definition for state machines based on Petri Nets that are able to execute concurrent actions reliably, execute and interleave several plans at the same time, and provide an easy to use modelling language. We show their workings based on the example of Human-Robot Interaction in a shopping mall.