About this Research Topic
The human behavior and human communication process may be complex. This complexity is replicated in the Human-Machine Interaction process and it is connected with the characteristics of communication and human behavior since humans can transmit complex messages through different interaction modalities (e.g. voice, gesture, speech, gaze), and they are often driven by emotions that both influence and are influenced by their thoughts and interaction actions. Human-centered AI allows learning and understanding of the complexity of the human-machine interaction process. Human-centered AI aims to enhance human capabilities by providing intelligent and human-informed technologies. On the other hand, it learns human abilities as thinkers and shifts the cognitive load from humans to technology.
This is achieved using methods and algorithms that learn from human input and collaboration and are continuously improved by learning human behavior, emotions, and language. The term Human-Machine Interaction (HMI) refers both to Human-Computer Interaction and Human-Robot Interaction (HRI), and it should be focused on creating effective, efficient, safe, reliable, natural, and intuitive interaction between humans and machines. To this aim, the interaction system has to properly understand user needs, behavior, and emotion and the human agent has to easily interpret system behavior and engage with it in a trustworthy interaction. To guarantee a human-centered interaction process, several key aspects should be taken into account, such as: i) to provide the system with multimodal interfaces, that could be easily adapted to the specific context, user needs, and preferences, to make HMI natural and intuitive as possible, ii) to endow The AI with the capacity of handling dynamic, non-deterministic, and partially unknown environments, iii) to envisage a mutual understanding and learning to achieve transparent and explainable interaction, iv) to design systems that can understand and respond to human gestures, speech, and other non-verbal cues in a way that feels familiar and comfortable to humans.
To have a complete picture of the design and development process of currently available solutions is paramount for informing future AI-based systems. The introduction of human-centered AI aims at bridging the gap between machines and humans making AI really useful in improving human capabilities. The human-centered AI focuses on the people and allows understanding users and their contexts, as well as addressing users’ needs fitting their requirements and expectations, and adapting to human changes in terms of interaction behaviors. Human-centered AI is complex and involves various factors such as social and cultural behaviors, users’ abilities, preferences, and limitations. This implies the involvement of people in the interaction process design, learning from them, and collaborating with them to provide an accessible, effective, and sustainable interaction paradigm.
The scope is to provide an overview of AI techniques applied to human-machine interaction (HCI, HRI) mainly focusing on a human-centric approach. This research topic aims to explore and highlight the latest research challenges in AI-driven systems interacting with humans. It seeks to identify current advancements in the field with the goal of achieving a more effective and reliable human-machine interaction.
This Research Topic aims to take a cross-disciplinary perspective on human-machine interaction and its influence in several contexts (e.g., human-robot interaction, e-learning environment, healthcare). We encourage submissions that integrate dedicated findings on human-centric approaches from, e.g., medicine, sociology, psychology, and other relevant disciplines, into novel interaction design approaches. Original research, reviews, tools, and evaluation methods focused on the following topics, but are not limited to, are welcome:
-Human-centered design methodology and AI
- AI-based Human-robot interaction
- Trustworthy and explainable AI
- Intelligent human-machine collaboration
- Co-design and AI
- Collaborative methods and AI
- Multimodal interaction and AI
- Emotional artificial intelligence
- Human-centered design and accessibility
- Human-Centered design methodology and Acceptance of AI
- Methods and techniques for understanding human behavior
This is achieved using methods and algorithms that learn from human input and collaboration and are continuously improved by learning human behavior, emotions, and language. The term Human-Machine Interaction (HMI) refers both to Human-Computer Interaction and Human-Robot Interaction (HRI), and it should be focused on creating effective, efficient, safe, reliable, natural, and intuitive interaction between humans and machines. To this aim, the interaction system has to properly understand user needs, behavior, and emotion and the human agent has to easily interpret system behavior and engage with it in a trustworthy interaction. To guarantee a human-centered interaction process, several key aspects should be taken into account, such as: i) to provide the system with multimodal interfaces, that could be easily adapted to the specific context, user needs, and preferences, to make HMI natural and intuitive as possible, ii) to endow The AI with the capacity of handling dynamic, non-deterministic, and partially unknown environments, iii) to envisage a mutual understanding and learning to achieve transparent and explainable interaction, iv) to design systems that can understand and respond to human gestures, speech, and other non-verbal cues in a way that feels familiar and comfortable to humans.
To have a complete picture of the design and development process of currently available solutions is paramount for informing future AI-based systems. The introduction of human-centered AI aims at bridging the gap between machines and humans making AI really useful in improving human capabilities. The human-centered AI focuses on the people and allows understanding users and their contexts, as well as addressing users’ needs fitting their requirements and expectations, and adapting to human changes in terms of interaction behaviors. Human-centered AI is complex and involves various factors such as social and cultural behaviors, users’ abilities, preferences, and limitations. This implies the involvement of people in the interaction process design, learning from them, and collaborating with them to provide an accessible, effective, and sustainable interaction paradigm.
The scope is to provide an overview of AI techniques applied to human-machine interaction (HCI, HRI) mainly focusing on a human-centric approach. This research topic aims to explore and highlight the latest research challenges in AI-driven systems interacting with humans. It seeks to identify current advancements in the field with the goal of achieving a more effective and reliable human-machine interaction.
This Research Topic aims to take a cross-disciplinary perspective on human-machine interaction and its influence in several contexts (e.g., human-robot interaction, e-learning environment, healthcare). We encourage submissions that integrate dedicated findings on human-centric approaches from, e.g., medicine, sociology, psychology, and other relevant disciplines, into novel interaction design approaches. Original research, reviews, tools, and evaluation methods focused on the following topics, but are not limited to, are welcome:
-Human-centered design methodology and AI
- AI-based Human-robot interaction
- Trustworthy and explainable AI
- Intelligent human-machine collaboration
- Co-design and AI
- Collaborative methods and AI
- Multimodal interaction and AI
- Emotional artificial intelligence
- Human-centered design and accessibility
- Human-Centered design methodology and Acceptance of AI
- Methods and techniques for understanding human behavior
Keywords: AI based Human-Robot Interaction, Trustworthy and explainable AI, Intelligent human-machine collaboration, Co-design and AI, Collaborative methods and AI, Multimodal interaction and AI, Human-Centered design methodology and AI
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
