About this Research Topic
Computer system design has been driven predominantly by technical aspects and considerations. However, as computing systems are increasingly embedded and integrated into the world, they have an extended impact on individuals and societies. In this context, social and behavioral dynamics become equally essential components of computing systems research, notably including robotics and artificial intelligence. The increasing shift towards socio-technical systems and the integration of collaborative and cooperative aspects require new multidisciplinary efforts and a focus on the larger system context and evolution.
In hybrid systems, i.e., artificial agents, robots, and humans interacting with each other, explicitly considering the underlying social relations and dynamics is integral to the ability to design robust, adaptive, purpose- and useful systems.
Recent advances in machine learning and other computational techniques allow for the effective real-time analysis of complex interaction behavior. This holds both for the individual constituents as well as for the integration of subsystems.
As human-machine systems are deployed and becoming a reality in numerous scenarios involving different stakeholders, there is a need to shift system design towards more holistic approaches, which consider second-order effects on the host environment (“habitat”) and, in consequence, on the embedded systems. This explicitly includes long periods of time where systems of systems form ecologies and co-evolve after deployment.
Highly interactive settings, such as an advanced smart city scenario, comprise many heterogeneous systems, e.g., robots and/or artificial agents, crowd-sourced data, social media, location-based applications, swarms of delivery drones, cleaning or gardening robots. The goal is to improve urban life, such as transport systems, healthcare, infrastructure, and services, by collecting and analyzing data from a wide range of sensors and applications. However, many of these systems fail to adapt or serve their intended purpose within the larger socio-technical ecosystem as they are oblivious to the complex dynamics in their immediate context, let alone the effects on cities as larger organisms.
It is therefore vital to establish an integral way of designing human-machine systems that form socio-technical ecologies, allowing them to respect and adapt to the ever-evolving context in which they are embedded.
This Research Topic takes a systemic, cross-disciplinary perspective on human-AI and human-robot interaction, as well as their effects on the larger system context. We encourage submissions that integrate dedicated findings on socio-technical settings from, e.g., neuroscience, psychology, sociology, economics, and other relevant disciplines, into novel interaction and system design approaches. Original research, reviews, tools, databases, benchmarks, and evaluation methods relative to the following topics are welcome.
• Self-adaptation and co-evolution in hybrid socio-technical systems
• Methodologies and techniques for predicting, evaluating and adapting hybrid human-machine ecosystems,
on the short, medium, and long terms
• Flexibility and boundaries of human-machine systems in multicultural environments
• Mediated reality, interaction, and telepresence
• User acceptance, white-box/explainable AI (XAI)
• Trust, norms and reputation models and management
• Real-time monitoring and prediction of collective behavior
• Large-scale robot systems, swarms and human-swarm interaction
In hybrid systems, i.e., artificial agents, robots, and humans interacting with each other, explicitly considering the underlying social relations and dynamics is integral to the ability to design robust, adaptive, purpose- and useful systems.
Recent advances in machine learning and other computational techniques allow for the effective real-time analysis of complex interaction behavior. This holds both for the individual constituents as well as for the integration of subsystems.
As human-machine systems are deployed and becoming a reality in numerous scenarios involving different stakeholders, there is a need to shift system design towards more holistic approaches, which consider second-order effects on the host environment (“habitat”) and, in consequence, on the embedded systems. This explicitly includes long periods of time where systems of systems form ecologies and co-evolve after deployment.
Highly interactive settings, such as an advanced smart city scenario, comprise many heterogeneous systems, e.g., robots and/or artificial agents, crowd-sourced data, social media, location-based applications, swarms of delivery drones, cleaning or gardening robots. The goal is to improve urban life, such as transport systems, healthcare, infrastructure, and services, by collecting and analyzing data from a wide range of sensors and applications. However, many of these systems fail to adapt or serve their intended purpose within the larger socio-technical ecosystem as they are oblivious to the complex dynamics in their immediate context, let alone the effects on cities as larger organisms.
It is therefore vital to establish an integral way of designing human-machine systems that form socio-technical ecologies, allowing them to respect and adapt to the ever-evolving context in which they are embedded.
This Research Topic takes a systemic, cross-disciplinary perspective on human-AI and human-robot interaction, as well as their effects on the larger system context. We encourage submissions that integrate dedicated findings on socio-technical settings from, e.g., neuroscience, psychology, sociology, economics, and other relevant disciplines, into novel interaction and system design approaches. Original research, reviews, tools, databases, benchmarks, and evaluation methods relative to the following topics are welcome.
• Self-adaptation and co-evolution in hybrid socio-technical systems
• Methodologies and techniques for predicting, evaluating and adapting hybrid human-machine ecosystems,
on the short, medium, and long terms
• Flexibility and boundaries of human-machine systems in multicultural environments
• Mediated reality, interaction, and telepresence
• User acceptance, white-box/explainable AI (XAI)
• Trust, norms and reputation models and management
• Real-time monitoring and prediction of collective behavior
• Large-scale robot systems, swarms and human-swarm interaction
Keywords: Human-AI Interaction, Human-Robot Interaction, Systems Engineering
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