Definitions of engagement in human-agent interaction

Glas, Nadine; Pélachaud, Catherine

doi:10.1109/acii.2015.7344688

Cited by 49 publications

(33 citation statements)

References 45 publications

(63 reference statements)

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“…al. [11]. Poggi describes engagement as: "the value that a participant in an interaction attributes to the goal of being together with the other participant(s) and of continuing the interaction" [12].…”

Section: Engagement In Interactionsmentioning

confidence: 99%

Speech Driven Backchannel Generation Using Deep Q-Network for Enhancing Engagement in Human-Robot Interaction

et al. 2019

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We present a novel method for training a social robot to generate backchannels during human-robot interaction. We address the problem within an off-policy reinforcement learning framework, and show how a robot may learn to produce non-verbal backchannels like laughs, when trained to maximize the engagement and attention of the user. A major contribution of this work is the formulation of the problem as a Markov decision process (MDP) with states defined by the speech activity of the user and rewards generated by quantified engagement levels. The problem that we address falls into the class of applications where unlimited interaction with the environment is not possible (our environment being a human) because it may be time-consuming, costly, impracticable or even dangerous in case a bad policy is executed. Therefore, we introduce deep Q-network (DQN) in a batch reinforcement learning framework, where an optimal policy is learned from a batch data collected using a more controlled policy. We suggest the use of human-to-human dyadic interaction datasets as a batch of trajectories to train an agent for engaging interactions. Our experiments demonstrate the potential of our method to train a robot for engaging behaviors in an offline manner.

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Section: Engagement In Interactionsmentioning

confidence: 99%

Speech Driven Backchannel Generation Using Deep Q-Network for Enhancing Engagement in Human-Robot Interaction

et al. 2019

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“…Despite of being a major theme of research and a universal goal in Human-Computer Interaction (HCI), engagement is a difficult concept to define (102 different definitions of engagement exist according to Doherty and Doherty review Doherty and Doherty, 2018), due to its multidimensional nature and the difficulty to measure it. A detailed summary of engagement definitions in humanagent interaction is provided in Glas and Pelachaud (2015a). Among others, it can be defined as "the value that a participant in an interaction attributes to the goal of being together with the other participant(s) and of continuing the interaction" (Poggi, 2007), and as "the process by which participants involved in an interaction start, maintain and terminate an interaction" (Sidner and Dzikovska, 2005;Corrigan et al, 2016).…”

Section: Engagement In Human-agent Interactionmentioning

confidence: 99%

Managing an Agent's Self-Presentational Strategies During an Interaction

Biancardi¹,

Mancini

Lerner

et al. 2019

Front. Robot. AI

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In this paper we present a computational model for managing the impressions of warmth and competence (the two fundamental dimensions of social cognition) of an Embodied Conversational Agent (ECA) while interacting with a human. The ECA can choose among four different self-presentational strategies eliciting different impressions of warmth and/or competence in the user, through its verbal and non-verbal behavior. The choice of the non-verbal behaviors displayed by the ECA relies on our previous studies. In our first study, we annotated videos of human-human natural interactions of an expert on a given topic talking to a novice, in order to find associations between the warmth and competence elicited by the expert's non-verbal behaviors (such as type of gestures, arms rest poses, smiling). In a second study, we investigated whether the most relevant non-verbal cues found in the previous study were perceived in the same way when displayed by an ECA. The computational learning model presented in this paper aims to learn in real-time the best strategy (i.e., the degree of warmth and/or competence to display) for the ECA, that is, the one which maximizes user's engagement during the interaction. We also present an evaluation study, aiming to investigate our model in a real context. In the experimental scenario, the ECA plays the role of a museum guide introducing an exposition about video games. We collected data from 75 visitors of a science museum. The ECA was displayed in human dimension on a big screen in front of the participant, with a Kinect on the top. During the interaction, the ECA could adopt one of 4 self-presentational strategies during the whole interaction, or it could select one strategy randomly for each speaking turn, or it could use a reinforcement learning algorithm to choose the strategy having the highest reward (i.e., user's engagement) after each speaking turn.

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“…Many of the existing studies on engagement have concentrated on the notion of engagement [2,3], while the others have primarily focused on its measurement, detection and improvement in HCI. A recent survey summarizes the issues regarding engagement in human-agent interactions and presents an application on engagement improvement in GRETA/VIB platform [4].…”

Section: Related Workmentioning

confidence: 99%

Analysis of Engagement and User Experience with a Laughter Responsive Social Robot

et al. 2017

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We explore the effect of laughter perception and response in terms of engagement in human-robot interaction. We designed two distinct experiments in which the robot has two modes: laughter responsive and laughter non-responsive. In responsive mode, the robot detects laughter using a multimodal real-time laughter detection module and invokes laughter as a backchannel to users accordingly. In non-responsive mode, robot has no utilization of detection, thus provides no feedback. In the experimental design, we use a straightforward question-answer based interaction scenario using a back-projected robot head. We evaluate the interactions with objective and subjective measurements of engagement and user experience.

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Definitions of engagement in human-agent interaction

Cited by 49 publications

References 45 publications

Speech Driven Backchannel Generation Using Deep Q-Network for Enhancing Engagement in Human-Robot Interaction

Speech Driven Backchannel Generation Using Deep Q-Network for Enhancing Engagement in Human-Robot Interaction

Managing an Agent's Self-Presentational Strategies During an Interaction

Analysis of Engagement and User Experience with a Laughter Responsive Social Robot

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