A Model of a Social Chatbot

Augello, Agnese; Gentile, Manuel; Weideveld, Lucas; Dignum, Frank

doi:10.1007/978-3-319-39345-2_57

Cited by 21 publications

(10 citation statements)

References 19 publications

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“…Recent work in natural language interaction has presented models that can converse by predicting the next sentence given the previous sentence or sentences in a conversation [40]. And proposed models of a social chatbot that can choose the most suitable dialogue plans according to "social practice", for a communicative skill learning game [2].…”

Section: Related Workmentioning

confidence: 99%

How Do You Want Your Chatbot? An Exploratory Wizard-of-Oz Study with Young, Urban Indians

Thies

Menon

Magapu

et al. 2017

Lecture Notes in Computer Science

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As text-messaging chatbots become increasingly "human", it will be important to understand the personal interactions that users are seeking with a chatbot. What chatbot personalities are most compelling to young, urban users in India? To explore this question, we first conducted exploratory Wizard-of-Oz (WoZ) studies with 14 users that simulated interactions with a hypothetical chatbot. We evaluated three personalities for the chatbot-Maya, a productivity oriented bot with nerd wit; Ada, a fun, flirtatious bot; and Evi, an emotional buddy bot. We followed up with one-on-one interviews with the users discussing their experiences with each of the chatbots, what they liked, and what they did not. Overall our results show that users wanted a chatbot like Maya, who could add value to their life while being a friend, by making useful recommendations. But they also wanted preferred traits of Ada and Evi infused into Maya.

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Section: Related Workmentioning

confidence: 99%

How Do You Want Your Chatbot? An Exploratory Wizard-of-Oz Study with Young, Urban Indians

Thies

Menon

Magapu

et al. 2017

Lecture Notes in Computer Science

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“…Dialog considers both social and individual processes. Augello proposed a chatbot model that is able to choose the most suitable dialogue plans according to appropriate social practice [48]. The research in human-computer interaction on the chatbot dialogue system equipped with an audio-video interface shows promising results.…”

Section: Patient Carementioning

confidence: 99%

Machine Learning in Healthcare Communication

2021

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Machine learning (ML) is a study of computer algorithms for automation through experience. ML is a subset of artificial intelligence (AI) that develops computer systems, which are able to perform tasks generally having need of human intelligence. While healthcare communication is important in order to tactfully translate and disseminate information to support and educate patients and public, ML is proven applicable in healthcare with the ability for complex dialogue management and conversational flexibility. In this topical review, we will highlight how the application of ML/AI in healthcare communication is able to benefit humans. This includes chatbots for the COVID-19 health education, cancer therapy, and medical imaging.

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“…Currently, chatbots do not have the ability to correctly handle conversations based on the social context. Authors in [19] propose a chatbot model that can choose suitable dialogues, according to what the sociological literature refers to as ''social practice''.…”

Section: A Chatbotsmentioning

confidence: 99%

An AI Approach to Collecting and Analyzing Human Interactions With Urban Environments

et al. 2019

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Thanks to advances in Internet of Things and crowd-sensing, it is possible to collect vast amounts of urban data, to better understand how citizens interact with cities and, in turn, improve human well-being in urban environments. This is a scientifically challenging proposition, as it requires new methods to fuse objective (heterogeneous) data (e.g. people location trails and sensors data) with subjective (perceptual) data (e.g. the citizens' quality of experience collected through feedback forms). When it comes to vast urban areas, collecting statistically significant data is a daunting task; thus new data-collection methods are required too. In this work, we turn to artificial intelligence (AI) to address these challenges, introducing a method whereby the objective, sensor data is analyzed in real-time to scope down the test matrix of the subjective questionnaires. In turn, subjective responses are parsed through AI models to extract further objective information. The outcome is an interactive data analysis framework for urban environments, which we put to test in the context of a citizens' well-being project. In our pilot study, each new entry (objective or subjective) is parsed through the AI engine to determine which action maximizes the information gain. This translates into a particular question being fired at a specific moment and place, to a specific person. With our AI data collection method, we can reach statistical significance much faster, achieving (in our city-wide pilot study) a 41% acceleration factor and a 75% reduction in intrusiveness. Our study opens new avenues in urban science, with potential applications in urban planning, citizen's well-being projects, and sociology, to mention but a few cases.

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A Model of a Social Chatbot

Cited by 21 publications

References 19 publications

How Do You Want Your Chatbot? An Exploratory Wizard-of-Oz Study with Young, Urban Indians

How Do You Want Your Chatbot? An Exploratory Wizard-of-Oz Study with Young, Urban Indians

Machine Learning in Healthcare Communication

An AI Approach to Collecting and Analyzing Human Interactions With Urban Environments

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