scholarly journals When Agents Become Partners: A Review of the Role the Implicit Plays in the Interaction with Artificial Social Agents

2020 ◽  
Vol 4 (4) ◽  
pp. 81
Author(s):  
Sanobar Dar ◽  
Ulysses Bernardet
Keyword(s):  
Human Interaction ◽  
Social Agents ◽  
Social Agent ◽  
Relaxation System ◽  
Human Agent ◽  
Nonverbal Interaction ◽  
Agent Interaction ◽  
Social Tasks ◽  
Interaction Domains ◽  
The One

The way we interact with computers has significantly changed over recent decades. However, interaction with computers still falls behind human to human interaction in terms of seamlessness, effortlessness, and satisfaction. We argue that simultaneously using verbal, nonverbal, explicit, implicit, intentional, and unintentional communication channels addresses these three aspects of the interaction process. To better understand what has been done in the field of Human Computer Interaction (HCI) in terms of incorporating the type channels mentioned above, we reviewed the literature on implicit nonverbal interaction with a specific emphasis on the interaction between humans on the one side, and robot and virtual humans on the other side. These Artificial Social Agents (ASA) are increasingly used as advanced tools for solving not only physical but also social tasks. In the literature review, we identify domains of interaction between humans and artificial social agents that have shown exponential growth over the years. The review highlights the value of incorporating implicit interaction capabilities in Human Agent Interaction (HAI) which we believe will lead to satisfying human and artificial social agent team performance. We conclude the article by presenting a case study of a system that harnesses subtle nonverbal, implicit interaction to increase the state of relaxation in users. This “Virtual Human Breathing Relaxation System” works on the principle of physiological synchronisation between a human and a virtual, computer-generated human. The active entrainment concept behind the relaxation system is generic and can be applied to other human agent interaction domains of implicit physiology-based interaction.

Agents and Social Interaction Insights from Social Psychology

2010 ◽  
pp. 74-91
Author(s):  
Joseph C. Bullington
Keyword(s):  
Social Psychology ◽  
Social Interaction ◽  
Social Perception ◽  
Attribution Theory ◽  
Interpersonal Relations ◽  
Human Interaction ◽  
Artificial Agents ◽  
Agent Based ◽  
Human Agent ◽  
Agent Interaction

Social interaction represents a powerful new locus of research in the quest to build more truly humanlike artificial agents. The work in this area, as in the field of human computer interaction, generally, is becoming more interdisciplinary in nature. In this spirit, the present chapter will survey concepts and theory from social psychology, a field many researchers may be unfamiliar with. Dennett’s notion of the intentional system will provide some initial grounding for the notion of social interaction, along with a brief discussion of conversational agents. The body of the chapter will then survey the areas of animal behavior and social psychology most relevant to human-agent interaction, concentrating on the areas of interpersonal relations and social perception. Within the area of social perception, the focus will be on the topics of emotion and attribution theory. Where relevant, research in the area of agent-human interaction will be discussed. The chapter will conclude with a brief survey of the use of agent-based modeling and simulation in social theory. The future looks very promising for researchers in this area; the complex problems involved in developing artificial agents who have mind-like attributes will require an interdisciplinary effort.

Agents and Social Interaction

2009 ◽  
pp. 35-50
Author(s):  
Joseph C. Bullington
Keyword(s):  
Social Psychology ◽  
Social Interaction ◽  
Social Perception ◽  
Attribution Theory ◽  
Interpersonal Relations ◽  
Human Interaction ◽  
Artificial Agents ◽  
Agent Based ◽  
Human Agent ◽  
Agent Interaction

Social interaction represents a powerful new locus of research in the quest to build more truly human-like artificial agents. The work in this area, as in the field of human computer interaction, generally, is becoming more interdisciplinary in nature. In this spirit, the present chapter will survey concepts and theory from social psychology, a field many researchers may be unfamiliar with. Dennett’s notion of the intentional system will provide some initial grounding for the notion of social interaction, along with a brief discussion of conversational agents. The body of the chapter will then survey the areas of animal behavior and social psychology most relevant to human-agent interaction, concentrating on the areas of interpersonal relations and social perception. Within the area of social perception, the focus will be on the topics of emotion and attribution theory. Where relevant, research in the area of agent-human interaction will be discussed. The chapter will conclude with a brief survey of the use of agent-based modeling and simulation in social theory. The future looks very promising for researchers in this area; the complex problems involved in developing artificial agents who have mind-like attributes will require an interdisciplinary effort.

scholarly journals Adaptation Mechanisms in Human–Agent Interaction: Effects on User’s Impressions and Engagement

2021 ◽  
Vol 3 ◽  
Author(s):  
Beatrice Biancardi ◽  
Soumia Dermouche ◽  
Catherine Pelachaud
Keyword(s):  
A Priori ◽  
Experimental Studies ◽  
Human Interaction ◽  
Virtual Agents ◽  
Conversational Agents ◽  
Adaptive Agents ◽  
Human Agent ◽  
Adaptation Mechanisms ◽  
Agent Interaction ◽  
The Impact

Adaptation is a key mechanism in human–human interaction. In our work, we aim at endowing embodied conversational agents with the ability to adapt their behavior when interacting with a human interlocutor. With the goal to better understand what the main challenges concerning adaptive agents are, we investigated the effects on the user’s experience of three adaptation models for a virtual agent. The adaptation mechanisms performed by the agent take into account the user’s reaction and learn how to adapt on the fly during the interaction. The agent’s adaptation is realized at several levels (i.e., at the behavioral, conversational, and signal levels) and focuses on improving the user’s experience along different dimensions (i.e., the user’s impressions and engagement). In our first two studies, we aim to learn the agent’s multimodal behaviors and conversational strategies to dynamically optimize the user’s engagement and impressions of the agent, by taking them as input during the learning process. In our third study, our model takes both the user’s and the agent’s past behavior as input and predicts the agent’s next behavior. Our adaptation models have been evaluated through experimental studies sharing the same interacting scenario, with the agent playing the role of a virtual museum guide. These studies showed the impact of the adaptation mechanisms on the user’s experience of the interaction and their perception of the agent. Interacting with an adaptive agent vs. a nonadaptive agent tended to be more positively perceived. Finally, the effects of people’s a priori about virtual agents found in our studies highlight the importance of taking into account the user’s expectancies in human–agent interaction.

Surprise and human-agent interactions

2015 ◽  
Vol 13 (2) ◽  
pp. 461-477 ◽  
Author(s):  
Chloé Clavel
Keyword(s):  
Speech Processing ◽  
Affective Computing ◽  
Computational Models ◽  
Conversational Agents ◽  
Agent Interactions ◽  
Human Agent ◽  
Agent Interaction ◽  
Emotional Component ◽  
Emotional Behaviour ◽  
The One

Affective Computing aims at improving the naturalness of human-computer interactions by integrating the socio-emotional component in the interaction. The use of embodied conversational agents (ECAs) – virtual characters interacting with humans – is a key answer to this issue. On the one hand, the ECA has to take into account the human emotional behaviours and social attitudes. On the other hand, the ECA has to display socio-emotional behaviours with relevance. In this paper, we provide an overview of computational methods used for user’s socio-emotional behaviour analysis and of human-agent interaction strategies by questioning the ambivalent status of surprise. We focus on the computational models and on the methods we use to detect user’s emotion through language and speech processing and present a study investigating the role of surprise in the ECA’s answer.

Fuzzy Logic Usage in Emotion Communication of Human Machine Interaction

2011 ◽  
pp. 545-552
Author(s):  
Zhe Xu ◽  
David John ◽  
Anthony C. Boucouvalas
Keyword(s):  
Human Computer Interaction ◽  
Facial Expressions ◽  
New Technologies ◽  
The Internet ◽  
Mediated Communication ◽  
Human Agent ◽  
Agent Interaction ◽  
Human Participants ◽  
The One ◽  
Computer Interaction

As the popularity of the Internet has expanded, an increasing number of people spend time online. More than ever, individuals spend time online reading news, searching for new technologies, and chatting with others. Although the Internet was designed as a tool for computational calculations, it has now become a social environment with computer-mediated communication (CMC). Picard and Healey (1997) demonstrated the potential and importance of emotion in human-computer interaction, and Bates (1992) illustrated the roles that emotion plays in user interactions with synthetic agents. Is emotion communication important for human-computer interaction? Scott and Nass (2002) demonstrated that humans extrapolate their interpersonal interaction patterns onto computers. Humans talk to computers, are angry with them, and even make friends with them. In our previous research, we demonstrated that social norms applied in our daily life are still valid for human-computer interaction. Furthermore, we proved that providing emotion visualisation in the human-computer interface could significantly influence the perceived performances and feelings of humans. For example, in an online quiz environment, human participants answered questions and then a software agent judged the answers and presented either a positive (happy) or negative (sad) expression. Even if two participants performed identically and achieved the same number of correct answers, the perceived performance for the one in the positive-expression environment is significantly higher than the one in the negative-expression environment (Xu, 2005). Although human emotional processes are much more complex than in the above example and it is difficult to build a complete computational model, various models and applications have been developed and applied in human-agent interaction environments such as the OZ project (Bates, 1992), the Cathexis model (Velasquez, 1997), and Elliot’s (1992) affective reasoner. We are interested in investigating the influences of emotions not only for human-agent communication, but also for online human-human communications. The first question is, can we detect a human’s emotional state automatically and intelligently? Previous works have concluded that emotions can be detected in various ways—in speech, in facial expressions, and in text—for example, investigations that focus on the synthesis of facial expressions and acoustic expression including Kaiser and Wehrle (2000), Wehrle, Kaiser, Schmidt, and Scherer (2000), and Zentner and Scherer (1998). As text is still dominating online communications, we believe that emotion detection in textual messages is particularly important.

Fuzzy Logic Usage in Emotion Communication of Human Machine Interaction

2006 ◽  
pp. 227-233 ◽  
Author(s):  
Zhe Xu ◽  
David John ◽  
Anthony C. Boucouvalas
Keyword(s):  
Human Computer Interaction ◽  
Facial Expressions ◽  
New Technologies ◽  
The Internet ◽  
Mediated Communication ◽  
Human Agent ◽  
Agent Interaction ◽  
Human Participants ◽  
The One ◽  
Computer Interaction

As the popularity of the Internet has expanded, an increasing number of people spend time online. More than ever, individuals spend time online reading news, searching for new technologies, and chatting with others. Although the Internet was designed as a tool for computational calculations, it has now become a social environment with computer-mediated communication (CMC). Picard and Healey (1997) demonstrated the potential and importance of emotion in human-computer interaction, and Bates (1992) illustrated the roles that emotion plays in user interactions with synthetic agents. Is emotion communication important for human-computer interaction? Scott and Nass (2002) demonstrated that humans extrapolate their interpersonal interaction patterns onto computers. Humans talk to computers, are angry with them, and even make friends with them. In our previous research, we demonstrated that social norms applied in our daily life are still valid for human-computer interaction. Furthermore, we proved that providing emotion visualisation in the human-computer interface could significantly influence the perceived performances and feelings of humans. For example, in an online quiz environment, human participants answered questions and then a software agent judged the answers and presented either a positive (happy) or negative (sad) expression. Even if two participants performed identically and achieved the same number of correct answers, the perceived performance for the one in the positive-expression environment is significantly higher than the one in the negative-expression environment (Xu, 2005). Although human emotional processes are much more complex than in the above example and it is difficult to build a complete computational model, various models and applications have been developed and applied in human-agent interaction environments such as the OZ project (Bates, 1992), the Cathexis model (Velasquez, 1997), and Elliot’s (1992) affective reasoner. We are interested in investigating the influences of emotions not only for human-agent communication, but also for online human-human communications. The first question is, can we detect a human’s emotional state automatically and intelligently? Previous works have concluded that emotions can be detected in various ways—in speech, in facial expressions, and in text—for example, investigations that focus on the synthesis of facial expressions and acoustic expression including Kaiser and Wehrle (2000), Wehrle, Kaiser, Schmidt, and Scherer (2000), and Zentner and Scherer (1998). As text is still dominating online communications, we believe that emotion detection in textual messages is particularly important.

scholarly journals Intelligent Blended Agents: Reality–Virtuality Interaction with Artificially Intelligent Embodied Virtual Humans

2020 ◽  
Vol 4 (4) ◽  
pp. 85
Author(s):  
Susanne Schmidt ◽  
Oscar Ariza ◽  
Frank Steinicke
Keyword(s):  
Intelligent Agents ◽  
User Study ◽  
Physical Simulation ◽  
Body Representation ◽  
Human Interaction ◽  
Communication Process ◽  
Virtual Agents ◽  
Positive Effects ◽  
Human Agent ◽  
Agent Interaction

Intelligent virtual agents (VAs) already support us in a variety of everyday tasks such as setting up appointments, monitoring our fitness, and organizing messages. Adding a humanoid body representation to these mostly voice-based VAs has enormous potential to enrich the human–agent communication process but, at the same time, raises expectations regarding the agent’s social, spatial, and intelligent behavior. Embodied VAs may be perceived as less human-like if they, for example, do not return eye contact, or do not show a plausible collision behavior with the physical surroundings. In this article, we introduce a new model that extends human-to-human interaction to interaction with intelligent agents and covers different multi-modal and multi-sensory channels that are required to create believable embodied VAs. Theoretical considerations of the different aspects of human–agent interaction are complemented by implementation guidelines to support the practical development of such agents. In this context, we particularly emphasize one aspect that is distinctive of embodied agents, i.e., interaction with the physical world. Since previous studies indicated negative effects of implausible physical behavior of VAs, we were interested in the initial responses of users when interacting with a VA with virtual–physical capabilities for the first time. We conducted a pilot study to collect subjective feedback regarding two forms of virtual–physical interactions. Both were designed and implemented in preparation of the user study, and represent two different approaches to virtual–physical manipulations: (i) displacement of a robotic object, and (ii) writing on a physical sheet of paper with thermochromic ink. The qualitative results of the study indicate positive effects of agents with virtual–physical capabilities in terms of their perceived realism as well as evoked emotional responses of the users. We conclude with an outlook on possible future developments of different aspects of human–agent interaction in general and the physical simulation in particular.

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