Interactive Artificial Intelligence and Man-Machine Communication

Dear Colleagues,

Interactive artificial intelligence is based on the perception that human intelligence is characterized by interactivity. Many of the fascinating and core research issues in artificial intelligence involve topics for which the aim of smart and adaptive systems is to interact with people on their own terms. Indeed, in this digital era, digital systems are growing at an unprecedented rate. The wishes of users to interact with tailored content are ever increasing. This means that users are seeking intelligent software with greatly individualized user experiences (UXs), not only adaptive user interfaces (UIs). Therefore, the need for redefining traditional system development is of utmost importance. As such, incorporating sophisticated mechanisms into the development of robust systems, demonstrating the usefulness of this way of thinking, and into the development of fundamental algorithms, for disruptive technological features adjusted to human needs, is relevant. Such systems can exhibit a high degree of intelligent man–machine communication and UXs, user-centric features, and intelligence in their reasoning and diagnostic mechanisms. In recent decades, research efforts have focused on promoting man–machine communication and interactive artificial intelligence. In spite of the increased research interest, there is still room for further research on the directions of man–machine communication, interactivity, and artificial intelligence.

The present call-for-papers is requesting original research papers as well as review articles and short communications in the aforementioned areas. The topics of interest include, but are not limited to, the following:

• Human–computer interaction;
• Personalization and adaptivity in systems and services;
• Machine/deep/reinforcement learning;
• Collaborative and group work, communities of practice, and social networks;
• Immersive and virtual reality environments;
• Ubiquitous, mobile, and cloud environments;
• Adaptive support for navigation, models of users, diagnosis, reasoning, and feedback;
• The aspect of the modeling of motivation, metacognition, and affect;
• Affective computing; Applications of machine learning to address real-world problems.

Dr. Christos Troussas
Prof. Dr. Cleo Sgouropoulou
Dr. Akrivi Krouska
Prof. Dr. Ioannis Voyiatzis
Dr. Athanasios Voulodimos
Topic Editors