CoachAI: A Conversational Agent Assisted Health Coaching Platform

Poor lifestyle represents a health risk factor and is the leading cause of morbidity and chronic conditions. The impact of poor lifestyle can be significantly altered by individual behavior change. Although the current shift in healthcare towards a long lasting modifiable behavior, however, with increasing caregiver workload and individuals' continuous needs of care, there is a need to ease caregiver's work while ensuring continuous interaction with users. This paper describes the design and validation of CoachAI, a conversational agent assisted health coaching system to support health intervention delivery to individuals and groups. CoachAI instantiates a text based healthcare chatbot system that bridges the remote human coach and the users. This research provides three main contributions to the preventive healthcare and healthy lifestyle promotion: (1) it presents the conversational agent to aid the caregiver; (2) it aims to decrease caregiver's workload and enhance care given to users, by handling (automating) repetitive caregiver tasks; and (3) it presents a domain independent mobile health conversational agent for health intervention delivery. We will discuss our approach and analyze the results of a one month validation study on physical activity, healthy diet and stress management

Crowdsourcing for Reminiscence Chatbot Design

In this work-in-progress paper we discuss the challenges in identifying effective and scalable crowd-based strategies for designing content, conversation logic, and meaningful metrics for a reminiscence chatbot targeted at older adults. We formalize the problem and outline the main research questions that drive the research agenda in chatbot design for reminiscence and for relational agents for older adults in general

An Analysis of Mixed Initiative and Collaboration in Information-Seeking Dialogues

The ability to engage in mixed-initiative interaction is one of the core requirements for a conversational search system. How to achieve this is poorly understood. We propose a set of unsupervised metrics, termed ConversationShape, that highlights the role each of the conversation participants plays by comparing the distribution of vocabulary and utterance types. Using ConversationShape as a lens, we take a closer look at several conversational search datasets and compare them with other dialogue datasets to better understand the types of dialogue interaction they represent, either driven by the information seeker or the assistant. We discover that deviations from the ConversationShape of a human-human dialogue of the same type is predictive of the quality of a human-machine dialogue.Comment: SIGIR 2020 short conference pape

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

Chatbot for digital marketing and customer support: an artificial intelligence approach

Dissertação de mestrado em Computer ScienceHuman interaction with machines has never been so frequent as nowadays. In order to reduce the redundant workload of a human being that answers repeated and trivial questions regarding customer support on a digital marketing website, this work has the purpose of replacing this tedious job with an informatics tool, a dialogue tool. A dialogue tool like a Chatbot that could handle customer support to a digital marketing website, provides the opportunity of placing human resources on ”non mechanical tasks”. Given that Chatbots exchange messages directly with customers, they could collect required protocol information in all the interactions. In spite of the possibility of needing human assistance, he will not need to ask these standard questions and will improve its efficiency. By automating these required dialogues to answer questions about certain products, that would otherwise be responded by a human, the organizations will have the opportunity to place human resources in another sectors that are not so easily automated.A interação humana com máquinas nunca foi tão frequente como nos dias de hoje. Com a intenção de reduzir a quantidade de trabalho de um ser humano que receberia ao responder a questões triviais e repetidas no que diz respeito a Suporte ao Cliente, este trabalho tem o propósito de substituir um trabalho entediante por uma ferramenta informática, uma ferramenta que possibilite o diálogo entre o cliente e o serviço de suporte. Uma ferramenta como um Chatbot que poderia fornecer suporte ao cliente num website de marketing digital iria providenciar às empresas a oportunidade de alocar trabalhadores para tarefas ”menos mecânicas”. Dado que os Chatbots trocam mensagens diretamente com os clientes, estes podem recolher informações que são sempre necessárias e protocolares em todas as interações. Assim sendo, mesmo que este diálogo requira possivelmente um ser humano, este irá prescindir de fazer estas perguntas padrão, melhorando assim a eficiência deste trabalho (Suporte ao Cliente). Ao automatizar diálogos necessários para responder a questões acerca de produtos que, de outra forma seriam respondidas por um ser humano, as organizações estarão a poupar tempo e dinheiro que podem ser aplicados noutros sectores menos propícios a serem automatizados