Integrating Flow Theory and Adaptive Robot Roles: A Conceptual Model of Dynamic Robot Role Adaptation for the Enhanced Flow Experience in Long-term Multi-person Human-Robot Interactions

In this paper, we introduce a novel conceptual model for a robot's behavioral adaptation in its long-term interaction with humans, integrating dynamic robot role adaptation with principles of flow experience from psychology. This conceptualization introduces a hierarchical interaction objective grounded in the flow experience, serving as the overarching adaptation goal for the robot. This objective intertwines both cognitive and affective sub-objectives and incorporates individual and group-level human factors. The dynamic role adaptation approach is a cornerstone of our model, highlighting the robot's ability to fluidly adapt its support roles - from leader to follower - with the aim of maintaining equilibrium between activity challenge and user skill, thereby fostering the user's optimal flow experiences. Moreover, this work delves into a comprehensive exploration of the limitations and potential applications of our proposed conceptualization. Our model places a particular emphasis on the multi-person HRI paradigm, a dimension of HRI that is both under-explored and challenging. In doing so, we aspire to extend the applicability and relevance of our conceptualization within the HRI field, contributing to the future development of adaptive social robots capable of sustaining long-term interactions with humans

Artificial and Computational Intelligence in Games (Dagstuhl Seminar 12191)

This report documents the program and the outcomes of Dagstuhl Seminar 12191 "Artificial and Computational Intelligence in Games". The aim for the seminar was to bring together creative experts in an intensive meeting with the common goals of gaining a deeper understanding of various aspects of artificial and computational intelligence in games, to help identify the main challenges in game AI research and the most promising venues to deal with them. This was accomplished mainly by means of workgroups on 14 different topics (ranging from search, learning, and modeling to architectures, narratives, and evaluation), and plenary discussions on the results of the workgroups. This report presents the conclusions that each of the workgroups reached. We also added short descriptions of the few talks that were unrelated to any of the workgroups

Tech-Savvy Hospitality: A Strategic Approach to Overcoming Labor Shortages

The hospitality industry faces an unprecedented labor shortage, prompting a surge in technological solutions. This reflection paper explores how innovations such as robotics, artificial intelligence, and digital applications are being deployed to bridge these staffing gaps. By analyzing the role of technology in areas like housekeeping, guest services, event, and culinary operations, implications for service quality and industry standards are also assessed. While technology offers potential alleviation for labor challenges, it\u27s crucial to consider its impact on the inherent human-centric nature of hospitality

Robotics Technology in Mental Health Care

This chapter discusses the existing and future use of robotics and intelligent sensing technology in mental health care. While the use of this technology is nascent in mental health care, it represents a potentially useful tool in the practitioner's toolbox. The goal of this chapter is to provide a brief overview of the field, discuss the recent use of robotics technology in mental health care practice, explore some of the design issues and ethical issues of using robots in this space, and finally to explore the potential of emerging technology

An emotion and memory model for social robots : a long-term interaction

In this thesis, we investigate the role of emotions and memory in social robotic companions. In particular, our aim is to study the effect of an emotion and memory model towards sustaining engagement and promoting learning in a long-term interaction. Our Emotion and Memory model was based on how humans create memory under various emotional events/states. The model enabled the robot to create a memory account of user's emotional events during a long-term child-robot interaction. The robot later adapted its behaviour through employing the developed memory in the following interactions with the users. The model also had an autonomous decision-making mechanism based on reinforcement learning to select behaviour according to the user preference measured through user's engagement and learning during the task. The model was implemented on the NAO robot in two different educational setups. Firstly, to promote user's vocabulary learning and secondly, to inform how to calculate area and perimeter of regular and irregular shapes. We also conducted multiple long-term evaluations of our model with children at the primary schools to verify its impact on their social engagement and learning. Our results showed that the behaviour generated based on our model was able to sustain social engagement. Additionally, it also helped children to improve their learning. Overall, the results highlighted the benefits of incorporating memory during child-Robot Interaction for extended periods of time. It promoted personalisation and reflected towards creating a child-robot social relationship in a long-term interaction

Encoding the Enforcement of Safety Standards into Smart Robots to Harness Their Computing Sophistication and Collaborative Potential:A Legal Risk Assessment for European Union Policymakers

Until robots and humans mostly worked in fast-paced and yet separate environments, occupational health and safety (OHS) rules could address workers’ safety largely independently from robotic conduct. This is no longer the case: collaborative robots (cobots) working alongside humans warrant the design of policies ensuring the safety of both humans and robots at once, within shared spaces and upon delivery of cooperative workflows. Within the European Union (EU), the applicable regulatory framework stands at the intersection between international industry standards and legislation at the EU as well as Member State level. Not only do current standards and laws fail to satisfactorily attend to the physical and mental health challenges prompted by human–robot interaction (HRI), but they exhibit important gaps in relation to smart cobots (“SmaCobs”) more specifically. In fact, SmaCobs combine the black-box unforeseeability afforded by machine learning with more general HRI-associated risks, towards increasingly complex, mobile and interconnected operational interfaces and production chains. Against this backdrop, based on productivity and health motivations, we urge the encoding of the enforcement of OHS policies directly into SmaCobs. First, SmaCobs could harness the sophistication of quantum computing to adapt a tangled normative architecture in a responsive manner to the contingent needs of each situation. Second, entrusting them with OHS enforcement vis-à-vis both themselves and humans may paradoxically prove safer as well as more cost-effective than for humans to do so. This scenario raises profound legal, ethical and somewhat philosophical concerns around SmaCobs’ legal personality, the apportionment of liability and algorithmic explainability. The first systematic proposal to tackle such questions is henceforth formulated. For the EU, we propose that this is achieved through a new binding OHS Regulation aimed at the SmaCobs age.<br/

Five Lenses on Team Tutor Challenges: A Multidisciplinary Approach

This chapter describes five disciplinary domains of research or lenses that contribute to the design of a team tutor. We focus on four significant challenges in developing Intelligent Team Tutoring Systems (ITTSs), and explore how the five lenses can offer guidance for these challenges. The four challenges arise in the design of team member interactions, performance metrics and skill development, feedback, and tutor authoring. The five lenses or research domains that we apply to these four challenges are Tutor Engineering, Learning Sciences, Science of Teams, Data Analyst, and Human–Computer Interaction. This matrix of applications from each perspective offers a framework to guide designers in creating ITTSs