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.

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.

Contract Negotiation in E-Marketplaces

2009 ◽  
pp. 267-275
Author(s):  
Larbi Esmahi ◽  
Elarbi Badidi
Keyword(s):  
Customer Service ◽  
Intelligent Agents ◽  
Virtual Agents ◽  
Electronic Market ◽  
Market Place ◽  
Negotiation Strategy ◽  
Intelligent Computing ◽  
Contract Negotiation ◽  
Auction Models ◽  
Agent Interaction

The advancement in distributed and intelligent computing has facilitated the use of software agents for implementing e-services; most electronic market places offer their customers virtual agents that can do their bidding (i.e., eBay, onSale). E-transactions via shopping agents constitute a promising opportunity in the e-markets (Chen, Vahidov, & Kersten, 2004). It becomes relevant what kind of information and what kinds of bargain policies are used both by agents and by the market place. There are several steps for building e-business: (1) attracting the customer, (2) knowing how they buy, (3) making transactions, (4) perfecting orders, (5) giving effective customer service, (6) offering customers recourse for problems such as breakage or returns, and (7) providing a rapid conclusion such as electronic payment. In the distributed e-market paradigm, these functions are abstracted via agents representing both contractual parts. In recent years, many researchers in intelligent agents’ domain have focused on the design of market architectures for electronic commerce (Fikes, Engelmore, Farquhar, & Pratt, 1995; Schoop & Quix, 2001; Zwass, 1999), and on protocols governing the interaction of rational agents engaged in such transactions (Hogg & Jennings, 1997; Kersten & Lai, 2005). While providing support for direct agent interaction, existing architectures for multiagent virtual markets usually lack explicit facilities for handling negotiation protocols, since they do not provide such protocols as an integrated part of the framework. In this article we will discuss the problem of contract negotiation in e-marketplaces. In the next section, we will present related models commonly used to implement negotiation in e-markets, game theory models, auction models, and contract-net protocols. Then the following section continues with the presentation of a negotiation protocol based on dependency relations. We then present a negotiation strategy based on risk evaluation. The conclusion summarizes the article and paves the further way concerning the truth in the negotiation strategy and the use of temporal aspects on commitments and executions of contracts.

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 Age-Related Differences in the Evaluation of a Virtual Health Agent’s Appearance and Embodiment in a Health-Related Interaction: Experimental Lab Study (Preprint)

2019 ◽  
Author(s):  
Carolin Straßmann ◽  
Nicole C Krämer ◽  
Hendrik Buschmeier ◽  
Stefan Kopp
Keyword(s):  
Assistive Technologies ◽  
Target Group ◽  
Virtual Agents ◽  
Social Responses ◽  
Healthy Behaviors ◽  
Human Agent ◽  
Age Related ◽  
Agent Interaction ◽  
Health Related ◽  
Overall Evaluation

BACKGROUND Assistive technologies have become more important owing to the aging population, especially when they foster healthy behaviors. Because of their natural interface, virtual agents are promising assistants for people in need of support. To engage people during an interaction with these technologies, such assistants need to match the users´ needs and preferences, especially with regard to social outcomes. OBJECTIVE Prior research has already determined the importance of an agent’s appearance in a human-agent interaction. As seniors can particularly benefit from the use of virtual agents to maintain their autonomy, it is important to investigate their special needs. However, there are almost no studies focusing on age-related differences with regard to appearance effects. METHODS A 2×4 between-subjects design was used to investigate the age-related differences of appearance effects in a human-agent interaction. In this study, 46 seniors and 84 students interacted in a health scenario with a virtual agent, whose appearance varied (cartoon-stylized humanoid agent, cartoon-stylized machine-like agent, more realistic humanoid agent, and nonembodied agent [voice only]). After the interaction, participants reported on the evaluation of the agent, usage intention, perceived presence of the agent, bonding toward the agent, and overall evaluation of the interaction. RESULTS The findings suggested that seniors evaluated the agent more positively (liked the agent more and evaluated it as more realistic, attractive, and sociable) and showed more bonding toward the agent regardless of the appearance than did students. In addition, interaction effects were found. Seniors reported the highest usage intention for the cartoon-stylized humanoid agent, whereas students reported the lowest usage intention for this agent. The same pattern was found for participant bonding with the agent. Seniors showed more bonding when interacting with the cartoon-stylized humanoid agent or voice only agent, whereas students showed the least bonding when interacting with the cartoon-stylized humanoid agent. CONCLUSIONS In health-related interactions, target group–related differences exist with regard to a virtual assistant’s appearance. When elderly individuals are the target group, a humanoid virtual assistant might trigger specific social responses and be evaluated more positively at least in short-term interactions.

Simulating socially intelligent agents in semantic virtual environments

2008 ◽  
Vol 23 (4) ◽  
pp. 369-388 ◽  
Author(s):  
Francisco Grimaldo ◽  
Miguel Lozano ◽  
Fernando Barber ◽  
Guillermo Vigueras
Keyword(s):  
Virtual Environments ◽  
Social Relations ◽  
Behavioral Problems ◽  
Intelligent Agents ◽  
Three Dimensional ◽  
The Other ◽  
Social Model ◽  
Virtual Agents ◽  
Artificial Society ◽  
Agent Interaction

AbstractThe simulation of synthetic humans inhabiting virtual environments is a current research topic with a great number of behavioral problems to be tackled. Semantical virtual environments (SVEs) have recently been proposed not only to ease world modeling but also to enhance the agent–object and agent–agent interaction. Thus, we propose the use of ontologies to define the world’s knowledge base and to introduce semantic levels of detail that help the sensorization of complex scenes—containing lots of interactive objects. The object taxonomy also helps to create general and reusable operativity for autonomous characters—for example, liquids can be poured from containers such as bottles. On the other hand, we use the ontology to define social relations among agents within an artificial society. These relations must be taken into account in order to display socially acceptable decisions. Therefore, we have implemented a market-based social model that reaches coordination and sociability by means of task exchanges. This paper presents a multi-agent framework oriented to simulate socially intelligent characters in SVEs. The framework has been successfully tested in three-dimensional (3D) dynamic scenarios while simulating a virtual university bar, where groups of waiters and customers interact with both the objects in the scene and the other virtual agents, finally displaying complex social behaviors.

scholarly journals Age-Related Differences in the Evaluation of a Virtual Health Agent’s Appearance and Embodiment in a Health-Related Interaction: Experimental Lab Study

10.2196/13726 ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. e13726
Author(s):  
Carolin Straßmann ◽  
Nicole C Krämer ◽  
Hendrik Buschmeier ◽  
Stefan Kopp
Keyword(s):  
Assistive Technologies ◽  
Target Group ◽  
Virtual Agents ◽  
Social Responses ◽  
Healthy Behaviors ◽  
Human Agent ◽  
Age Related ◽  
Agent Interaction ◽  
Health Related ◽  
Overall Evaluation

Background Assistive technologies have become more important owing to the aging population, especially when they foster healthy behaviors. Because of their natural interface, virtual agents are promising assistants for people in need of support. To engage people during an interaction with these technologies, such assistants need to match the users´ needs and preferences, especially with regard to social outcomes. Objective Prior research has already determined the importance of an agent’s appearance in a human-agent interaction. As seniors can particularly benefit from the use of virtual agents to maintain their autonomy, it is important to investigate their special needs. However, there are almost no studies focusing on age-related differences with regard to appearance effects. Methods A 2×4 between-subjects design was used to investigate the age-related differences of appearance effects in a human-agent interaction. In this study, 46 seniors and 84 students interacted in a health scenario with a virtual agent, whose appearance varied (cartoon-stylized humanoid agent, cartoon-stylized machine-like agent, more realistic humanoid agent, and nonembodied agent [voice only]). After the interaction, participants reported on the evaluation of the agent, usage intention, perceived presence of the agent, bonding toward the agent, and overall evaluation of the interaction. Results The findings suggested that seniors evaluated the agent more positively (liked the agent more and evaluated it as more realistic, attractive, and sociable) and showed more bonding toward the agent regardless of the appearance than did students. In addition, interaction effects were found. Seniors reported the highest usage intention for the cartoon-stylized humanoid agent, whereas students reported the lowest usage intention for this agent. The same pattern was found for participant bonding with the agent. Seniors showed more bonding when interacting with the cartoon-stylized humanoid agent or voice only agent, whereas students showed the least bonding when interacting with the cartoon-stylized humanoid agent. Conclusions In health-related interactions, target group–related differences exist with regard to a virtual assistant’s appearance. When elderly individuals are the target group, a humanoid virtual assistant might trigger specific social responses and be evaluated more positively at least in short-term interactions.

scholarly journals Voice in Human--Agent Interaction

2021 ◽  
Vol 54 (4) ◽  
pp. 1-43
Author(s):  
Katie Seaborn ◽  
Norihisa P. Miyake ◽  
Peter Pennefather ◽  
Mihoko Otake-Matsuura
Keyword(s):  
Intelligent Agents ◽  
Social Role ◽  
Conversational Agents ◽  
Human Agent ◽  
Classification Framework ◽  
The Social ◽  
Agent Interaction ◽  
User Groups ◽  
Computer Based

Social robots, conversational agents, voice assistants, and other embodied AI are increasingly a feature of everyday life. What connects these various types of intelligent agents is their ability to interact with people through voice. Voice is becoming an essential modality of embodiment, communication, and interaction between computer-based agents and end-users. This survey presents a meta-synthesis on agent voice in the design and experience of agents from a human-centered perspective: voice-based human--agent interaction (vHAI). Findings emphasize the social role of voice in HAI as well as circumscribe a relationship between agent voice and body, corresponding to human models of social psychology and cognition. Additionally, changes in perceptions of and reactions to agent voice over time reveals a generational shift coinciding with the commercial proliferation of mobile voice assistants. The main contributions of this work are a vHAI classification framework for voice across various agent forms, contexts, and user groups, a critical analysis grounded in key theories, and an identification of future directions for the oncoming wave of vocal machines.

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.

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