Chatbot

About: Chatbot is a research topic. Over the lifetime, 2415 publications have been published within this topic receiving 24372 citations. The topic is also known as: IM bot & AI chatbot.

Participatory Development and Pilot Testing of an Adolescent Health Promotion Chatbot.

Abstract: Background: The use of chatbots may increase engagement with digital behavior change interventions in youth by providing human-like interaction. Following a Person-Based Approach (PBA), integrating user preferences in digital tool development is crucial for engagement, whereas information on youth preferences for health chatbots is currently limited. Objective: The aim of this study was to gain an in-depth understanding of adolescents' expectations and preferences for health chatbots and describe the systematic development of a health promotion chatbot. Methods: Three studies in three different stages of PBA were conducted: (1) a qualitative focus group study (n = 36), (2) log data analysis during pretesting (n = 6), and (3) a mixed-method pilot testing (n = 73). Results: Confidentiality, connection to youth culture, and preferences when referring to other sources were important aspects for youth in chatbots. Youth also wanted a chatbot to provide small talk and broader support (e.g., technical support with the tool) rather than specifically in relation to health behaviors. Despite the meticulous approach of PBA, user engagement with the developed chatbot was modest. Conclusion: This study highlights that conducting formative research at different stages is an added value and that adolescents have different chatbot preferences than adults. Further improvement to build an engaging chatbot for youth may stem from using living databases.

Self-Adapting Chatbot Personalities for Better Peer Support

Abstract: Studies have shown that people relate better with other people who have similar personality characteristics as themselves. This is helpful in peer support scenarios where people should be receptive to receiving support and advice from others. In this paper we propose a chatbot personality model and an algorithm that enables the chatbot to adapt its personality in real-time as it interacts in conversation with the user. Our model is based on the Big Five personality model and we focus on two key personality traits, Extroversion and Agreeableness. The personality adaption algorithm uses an interactive genetic algorithm. We have exposed the chatbot to a controlled set of interactions and a user and the results show that the algorithms are capable of adapting personality traits to match the identified traits of the user. In the next phase of our experimentation we will expose the chatbot to healthcare professionals.

Smart College Chatbot using ML and Python

Abstract: The days of solely engaging with a service through a keyboard are over. Users interact with systems more and more through voice assistants and chatbots. A chatbot is a computer program that can converse with humans using Artificial Intelligence in messaging platforms. Every time the chatbot gets input from the user, it saves input and response which helps chatbot with little initial knowledge to evolve using gathered responses. With increased responses, precision of the chatbot also gets increases. The ultimate goal of this project is to add a chatbot feature and API for Matrusri Engineering College. This project will investigate how advancements in Artificial Intelligence and Machine Learning technology are being used to improve many services. Specifically it will look at development of chatbots as a channel for information distribution. The program selects the closest matching response from closest matching statement that matches input utilizing WordNet, it then chooses response from known selection of statements for that response. This project aimed to implement online chatbot system to assist users who access college website, using tools that expose Artificial Intelligence methods such as Natural Language Processing, allowing users to communicate with college chatbot using natural language input and to train chatbot using appropriate Machine Learning methods so it will be able to generate a response. There are numerous applications that are incorporating a human appearance and intending to simulate human dialog, yet in most part of the cases knowledge of chatbot is stored in a database created by a human expert.

Developing a Chatbot Using Machine Learning

Abstract: Now-a-days development of chatbot using different methods become trendier, till now many conversational chatbots were designed for the replacement of traditional chatbots. A chatbot is a software that is capable of communicating and performing actions like the way human do. Chatbot will give response for the query given by the user and it also capable of executing tasks. Chatbots developed in olden days are so difficult to perform task but chatbot developed in recent years are good in performance and its development also become easier because of wide availability of development platforms and wide availability of source code. There are many methods to develop chatbot, it can be developed using either natural language processing (NLP) or deep learning. In this project we developed a chatbot using machine learning which helps to give information about our college.

Multi-Turn Chatbot Based on Query-Context Attentions and Dual Wasserstein Generative Adversarial Networks

Abstract: To generate proper responses to user queries, multi-turn chatbot models should selectively consider dialogue histories. However, previous chatbot models have simply concatenated or averaged vector representations of all previous utterances without considering contextual importance. To mitigate this problem, we propose a multi-turn chatbot model in which previous utterances participate in response generation using different weights. The proposed model calculates the contextual importance of previous utterances by using an attention mechanism. In addition, we propose a training method that uses two types of Wasserstein generative adversarial networks to improve the quality of responses. In experiments with the DailyDialog dataset, the proposed model outperformed the previous state-of-the-art models based on various performance measures.