About this Research Topic
Human-to-machine communication approaches and solutions have been developing rapidly in recent decades. In this research topic, we focus on socially intelligent user-to-service (CS) communications (we use the expression user and service in this description meaning user to any intelligent device communication) capable of sustaining continuous communication with the user. Examples of such future CSs are interactive maps, mobile apps to help user doing recreative sport, etc. Software and design solutions (UX design) have seen major progress. Also, the development of new CSs is constantly involving new application domains and, more importantly, a variety of new user groups. Consequently, user-to-CS developments are facing new challenges, and the state-of-the-art solutions for human-to-CS communications are far from their ultimate goal, i.e., a two-way natural form of a continuous communication (NC), which is unobtrusive, pleasant, self-motivating, user engaging, supporting and invisible to the user. One way to define the goal of human-to-service communication is to establish a natural communication as if the service side was a human, i.e., to attribute a minimal level of social intelligence to the service. This view defines the major guidelines of the research topic outlined below.
In the process of attributing social intelligence to the service, personalization and user-adaptation solutions resulted in a significant improvement to this communication, but a natural form of communication in a continuous flow is still far from being achieved. This is mainly due to the fact that the services are socially ignorant, i.e., they do not read, interpret, understand and utilize the user’s social signals (SS, a non-verbal part of the communication), they do not emit their own SSs, and they do not elicit the SSs of a user. In order to establish natural communications between the user and the service, the machine must intelligently adapt the communication (ways of user service interaction) and the service (the content provided) according to the social signals that are read from the user in real time.
The research questions being addressed are as follows:
1. What are applicable strategies of user interaction toward and optimal user support through the social intelligent of the service?
2. How to establish and sustain natural continuous communication among the user and the service based on the machine social intelligence?
3. How to engage users into the natural communication to the service and how to keep her attention?
4. How to build a psychological measurement based computational models of machine emotions?
5. How to build a machine personality to follow the optimal user interaction strategies?
6. How to find out which social signals are relevant for a given service in establishing elements socially intelligent behavior?
7. How to find out which interaction modalities and which channels in the user-to-service interaction are relevant for a given CS in establishing NCs;
8. How to define and design user-interface elements to amplify users’ SSs in a given CS;
Papers including experimental results with end users or providing the building blocks (such as computational models, social (nonverbal) signals operational definitions etc) required to perform such experiments are welcome. For the same reason, papers covering publically available test sets on user-toservice communications are also most welcome.
he topics include but are not limited to:
• Natural communication, natural and affective human to machine interaction
• Human to socially intelligent machine interaction design
• Social intelligence of machines
• Computer-mediated communication.
• Social signals and nonverbal communication.
• Physiological sensing based computational models of emotions and social signals
• Physiological sensing based computational models of user’s attention and engagement
• Computational models of QoE
• User annoyance and user engagement
• Recognition and elicitation of the user's SSs by the machine.
• Operational definitions of social (nonverbal) communication signs and behavior cues, user data annotation and weak ground truth
• Service adaptation in real time.
• User experience design (UX design).
• Representation of the long-term relationship between the user and the service (machine)
• Real context user experiments and datasets.
Keywords: user-adapted communication, affective communication, social intelligence, social signals, computational models, human-machine interaction
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