Motivations, Classification and Model Trial of Conversational Agents for Insurance Companies

Advances in artificial intelligence have renewed interest in conversational agents. So-called chatbots have reached maturity for industrial applications. German insurance companies are interested in improving their customer service and digitizing their business processes. In this work we investigate the potential use of conversational agents in insurance companies by determining which classes of agents are of interest to insurance companies, finding relevant use cases and requirements, and developing a prototype for an exemplary insurance scenario. Based on this approach, we derive key findings for conversational agent implementation in insurance companies.Comment: 12 pages, 6 figure, accepted for presentation at The International Conference on Agents and Artificial Intelligence 2019 (ICAART 2019

A Chatbot Framework for Yioop

Over the past few years, messaging applications have become more popular than Social networking sites. Instead of using a specific application or website to access some service, chatbots are created on messaging platforms to allow users to interact with companies’ products and also give assistance as needed. In this project, we designed and implemented a chatbot Framework for Yioop. The goal of the Chatbot Framework for Yioop project is to provide a platform for developers in Yioop to build and deploy chatbot applications. A chatbot is a web service that can converse with users using artificial intelligence in messaging platforms. Chatbots feel more like a human and it changes the interaction between people and computers. The Chatbot Framework enables developers to create chatbots and allows users to connect with them in the user chosen Yioop discussion channel. A developer can incorporate language skills within a chatbot by creating a knowledge base so that the chatbot understands user messages and reacts to them like a human. A knowledge base is created by using a language understanding web interface in Yioop

Rasa-ptbr-boilerplate : FLOSS project that enables brazilian portuguese chatbot development by non-experts

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade UnB Gama (FGA), Engenharia de Software, 2019.Chatbots possuem a capacidade de conversar com pessoas por meio de imitação do comportamento humano. Atualmente, chatbots são capazes de desempenhar tarefas simples como responder perguntas sobre um determinado contexto e desempenhar tarefas complexas como o gerenciamento completo de residências. No entanto, o desenvolvimento de um projeto de chatbot requer uma equipe completa formada por vários especialistas, que podem consumir tempo e recursos. É comum projetos de chatbots terem requisitos de software semelhantes e apenas se difenciar no domínio da solução específico o que poderia resultar na reutilização de software de código aberto (OSS) relacionado à chatbots. Neste trabalho, é examinado como os projetos de chatbot podem se beneficiar da reutilização no nível do projeto (reutilização de caixa preta). Foi demonstrado que é possível combinar estrategicamente a arquitetura e os diálogos com a utilização do modelo de processo CRISP-DM em novos contextos e propósitos de conversação. A principal contribuição deste trabalho é a apresentação de um projeto de chatbot chamado Rasa-ptbr-boilerplate com configurações e integrações de tecnologias voltado para a reutilização de forma que não especialistas sejam capazes de desenvolver um chatbot como caixa-preta.Chatbots have the ability to talk to people through the imitation of human behavior. Currently, chatbots are able to perform simple tasks such as answering questions about a particular context and performing complex tasks such as complete home management. However, the development of a chatbot project requires a full team of many experts, which can consume time and resources. It is common for chatbot projects to have similar software requirements and only to differ in the domain of the specific solution which could result in the re-use of open source software (OSS) related to chatbots. In this work, it is examined how chatbot projects can benefit from reuse at the project level (black box reuse). It has been shown that it is possible to strategically combine the architecture and dialogues with the use of CRISPDM process model in new contexts and conversational purposes. The main contribution of this work is the presentation of a chatbot project called Rasa-ptbr-boilerplate with configurations and integrations of technologies aimed at the reuse so that non-specialists are able to develop a chatbot as a black box

Chatbots as a novel access method for government open data

IIn this discussion paper, we propose to employ chatbots as a user-friendly interface for open data published by organizations, specifically focusing on public administrations. Open data are especially useful in e-Government initiatives but their exploitation is currently hampered to end users by the lack of user-friendly access methods. On the other hand, current UX in social networks have made people used to chatting. Building on cognitive technologies, we prototyped a chatbot on top of the OpenCantieri dataset published by the Italian Ministero delle Infrastrutture e Trasporti, and we argue that such a model can be extended as a generally available access method to open data

A virtual diary companion

Chatbots and embodied conversational agents show turn based conversation behaviour. In current research we almost always assume that each utterance of a human conversational partner should be followed by an intelligent and/or empathetic reaction of chatbot or embodied agent. They are assumed to be alert, trying to please the user. There are other applications which have not yet received much attention and which require a more patient or relaxed attitude, waiting for the right moment to provide feedback to the human partner. Being able and willing to listen is one of the conditions for being successful. In this paper we have some observations on listening behaviour research and introduce one of our applications, the virtual diary companion

Evorus: A Crowd-powered Conversational Assistant Built to Automate Itself Over Time

Crowd-powered conversational assistants have been shown to be more robust than automated systems, but do so at the cost of higher response latency and monetary costs. A promising direction is to combine the two approaches for high quality, low latency, and low cost solutions. In this paper, we introduce Evorus, a crowd-powered conversational assistant built to automate itself over time by (i) allowing new chatbots to be easily integrated to automate more scenarios, (ii) reusing prior crowd answers, and (iii) learning to automatically approve response candidates. Our 5-month-long deployment with 80 participants and 281 conversations shows that Evorus can automate itself without compromising conversation quality. Crowd-AI architectures have long been proposed as a way to reduce cost and latency for crowd-powered systems; Evorus demonstrates how automation can be introduced successfully in a deployed system. Its architecture allows future researchers to make further innovation on the underlying automated components in the context of a deployed open domain dialog system.Comment: 10 pages. To appear in the Proceedings of the Conference on Human Factors in Computing Systems 2018 (CHI'18

Artificial Intelligence for the Financial Services Industry: What Challenges Organizations to Succeed?

As a research field, artificial intelligence (AI) exists for several years. More recently, technological breakthroughs, coupled with the fast availability of data, have brought AI closer to commercial use. Internet giants such as Google, Amazon, Apple or Facebook invest significantly into AI, thereby underlining its relevance for business models worldwide. For the highly data driven finance industry, AI is of intensive interest within pilot projects, still, few AI applications have been implemented so far. This study analyzes drivers and inhibitors of a successful AI application in the finance industry based on panel data comprising 22 semi-structured interviews with experts in AI in finance. As theoretical lens, we structured our results using the TOE framework. Guidelines for applying AI successfully reveal AI-specific role models and process competencies as crucial, before trained algorithms will have reached a quality level on which AI applications will operate without human intervention and moral concerns