Chatbots have become integral to various application domains, including those with safety-critical considerations. As a result, there is a pressing need for methods that ensure chatbots consistently adhere to expected, safe behaviours. In this paper, we introduce RV4Chatbot, a Runtime Verification framework designed to monitor deviations in chatbot behaviour. We formalise expected behaviours as interaction protocols between the user and the chatbot. We present the RV4Chatbot design and describe two implementations that instantiate it: RV4Rasa, for monitoring chatbots created with the Rasa framework, and RV4Dialogflow, for monitoring Dialogflow chatbots. Additionally, we detail experiments conducted in a factory automation scenario using both RV4Rasa and RV4Dialogflow.

University students often spend a considerable amount of time seeking answers to common questions from administrators or teachers. This can become tedious for both parties, leading to a need for a solution. In response, this paper proposes a chatbot model that utilizes natural language processing and deep learning techniques to answer frequently asked questions (FAQs) in the Amharic language. Chatbots are computer programs that simulate human conversation through the use of artificial intelligence (AI), acting as a virtual assistant to handle questions and other tasks. The proposed chatbot program employs tokenization, normalization, stop word removal, and stemming to analyze and categorize Amharic input sentences. Three machine learning model algorithms were used to classify tokens and retrieve appropriate responses: Support Vector Machine (SVM), Multinomial Na\"ive Bayes, and deep neural networks implemented through TensorFlow, Keras, and NLTK. The deep learning model achieved the best results with 91.55% accuracy and a validation loss of 0.3548 using an Adam optimizer and SoftMax activation function. The chatbot model was integrated with Facebook Messenger and deployed on a Heroku server for 24-hour accessibility. The experimental results demonstrate that the chatbot framework achieved its objectives and effectively addressed challenges such as Amharic Fidel variation, morphological variation, and lexical gaps. Future research could explore the integration of Amharic WordNet to narrow the lexical gap and support more complex questions.

In the next sections, you'll design and implement the backend framework of a typical chatbot from scratch. You will also explore some popular open-source chatbot frameworks such as Dialogflow and LUIS. The authors then explain how you can integrate various third-party services and enterprise databases with the custom chatbot framework. In the final section, you'll discuss how to deploy the custom chatbot framework on the AWS cloud.

I have built prototypes with most of the commercial cloud and opensource Conversational UI & AI platforms currently available. I have found that environments are generally very similar in their approach to tools available for crafting a conversational interface. Considering what's available, chatbot development environments can still be segmented into 4 distinct groups. The leading commercial cloud environments attract customers and users to them purely for their natural language processing prowess and presence. Among these I would count IBM Watson Assistant, Microsoft Bot Framework / Composer / LUIS / Virtual Agents, Google Dialog Flow etc. Established companies gravitate to these environments, at significant cost of course.

If you strip away all the bells, whistles, and tricks, there are only really two frameworks for chatbots -- NLP and Buttons. The "Buttons" style of chatbot is often referred to as a "rules-based" chatbot or perhaps a "menu" chatbot, "scripted" chatbot, or "tree" chatbot. It's what I personally refer to as a Plinko chatbot, which functions something like this: Meanwhile, the second fundamental chatbot framework is known as the "NLP" or "Natural Language Processing" style of chatbot. Sometimes, it's called NLU ("Natural Language Understanding). This second type of chatbot framework is always -- and I do mean always -- referred to as "AI" or "using AI" (and -- for bonus points -- Machine Learning (ML)) regardless of how much real AI is involved.

-- T owards conversational agents that are capable of handling more complex questions on contractual conditions, formalizing contract statements in a machine readable way is crucial. However, constructing a formal model which captures the full scope of a contract proves difficult due to the overall complexity its set of rules represent. Instead, this paper presents a top-down approach to the problem. A user-friendly tool we developed for this purpose allows to do so easily and at scale. Then, we expose the statements as service so they can get smoothly integrated in any chatbot framework. For a long time, researchers in artificial intelligence (AI) have been intrigued by the idea of developing a conversational agent that is capable of having a coherent conversation with humans [1]-[3]. Recent breakthroughs in semantics and speech recognition have given rise to hopes for robust solutions to the problem [4], [5]. Major information technology companies have released digital assistants and chatbot frameworks to facilitate the building of conversational agents [6], [7].

Chatbots offer a useful way to leverage the power of AI, and are now accessible for any size of application. The back-and-forth written nature of chatting is conducive to utilizing existing chatbot frameworks and AI models to automate interactions which would have required a whole team of agents just a short time ago. To demonstrate how easy it is, we'll use a chatbot framework and a sentiment analysis model from the Serverless AI Cloud -- both of which have free trial tiers. It only takes a few minutes, and it's free to get started. After signing up, click the "New Bot" button and pick "Blank Project": Note the column on the left called "All Topics".

In a previous post called "How to make your chatbot more human-like," I expanded on some of the most common issues users face while talking to a bot, and I explained how developers can solve them by using NLP. After conducting research and trying all the major bot development platforms, I realized the need for a long and intensive training to provide accurate answers to users' requests. Chatbot training is a resource intensive task. Linguistic analysis provides different solutions that speed up training and, most importantly, solve some structural issues with bot development. Most chatbot frameworks are based around the concept of intent and entity detection, which involves identifying both the intent of an utterance and the entities relevant to that intent.

Closed Domain Question with Retrieval Responses: This is a low hanging fruit area for chatbots to provide business value. We process over 1.5 billion calls a year for IVR business, which is the function that is occurring in Square 1. Chatbots could mirror the IVR functionality with an improved UI for a much better customer experience. We are also using this Square 1 approach to develop content delivery for mobile self-service capabilities when our My:Time digital engagement solution includes a mobile app.