191 research outputs found
XAI in HRI: A Journey to the Centre of the Explainability
No full textWe are witnessing the global spread of artificial intelligence (AI) technology in people’s
everyday lives. With the advent of the AI surge, the demand for explainable AI (XAI)
techniques arose due to the growing intricacy of AI models. Users sought to comprehend the
reasoning behind the decisions made by these models, a necessity that became more pressing
as an expanding number of AI-driven robots interacted with people in real-world scenarios.
Recent years have witnessed the XAI community recognizing the imperative of leveraging
the social dimensions of the explanation process. Drawing insights from psychology and
cognitive sciences, researchers are increasingly reframing explainability as a social problem.
This conceptual shift is exemplified by explainable robots capable of using the common
ground established with human partners during the explanation generation process.
Social autonomous robots can trigger neural and social mechanisms in people similar to
those happening during interactions between humans. Given the ease with which individuals
attribute intentions, beliefs, and even second-order theory of mind capabilities to robots, the
human-robot interaction (HRI) community is increasingly incorporating these mechanisms
into explanatory interchanges.
This thesis introduces a theoretical framework for XAI in HRI that leverages the social-
dialogical nature of explanations. The framework models the explanation process as a
dialogue between robots and human partners. Differently form existing approaches, our
framework emphasizes the influence of the human-robot common ground and interaction
history on the generation of explanations.
Subsequently, the framework’s philosophy is implemented in an HRI collaborative
decision-making scenario. The focus is on exploring how explanations based on shared
human-robot experiences impact individuals’ decision-making and the role of their personality traits in this context. Results showed that a social robot that justifies its suggestions
with explanations exploiting its common ground with the human partner is more persuasive
than classical explanations, especially for less skilled participants. Moreover, participants’
personality traits significantly impacted their decision-making and interaction with the robot.
Finally, to assess the effectiveness of such explanations compared to classical ones, an
evaluation task is designed to measure the informativeness of XAI systems for non-expert
users. This task is instantiated in various domains such as human-computer interaction
(HCI), HRI, and self-learning, examining how different types of explanations and artificial
explainable agents influence people’s learning of new tasks. Results showed that expert
explainable agents influenced participants’ learning, limiting them from adequately exploring
the learning environment, as participants who learned alone did.
Through this thesis, we advanced existing literature on collaborative decision-making
with both HCI and HRI domains. Employing methodologies derived from HRI, we compared
classical XAI techniques with explanation approaches that leverage on the human-robot
common ground. Our investigation highlighted how these latter approaches improve robot’s
persuasiveness, particularly in social collaborative contexts. Additionally, we conceptualized
and developed an assessment task to measure the quality of explanations. Our findings did
not highlight differences between classical and partner-aware explanation methodologies.
Nevertheless, results brought to light the influence that both robotic and artificial agents have
on people’s learning, limiting their exploration strategies
Author Correction: Gluten consumption and inflammation affect the development of celiac disease in at-risk children
No full textThe original version of this Article contained an error in the spelling of the authors Renata Auricchio, Ilaria Calabrese, Martina Galatola, Donatella Cielo, Fortunata Carbone, Marianna Mancuso, Giuseppe Matarese, Riccardo Troncone, Salvatore Auricchio & Luigi Greco which were incorrectly given as Auricchio Renata, Calabrese Ilaria, Galatola Martina, Cielo Donatella, Carbone Fortunata, Mancuso Marianna, Matarese Giuseppe, Troncone Riccardo, Auricchio Salvatore & Greco Luigi. The original article has been corrected
Natural Born Explainees: how users' personality traits shape the human-robot interaction with explainable robots
No full text<p>In this work, we performed a user study in which participants had to solve a human-robot teaming decision-making task (the Connect 4 game) with an explainable vs non-explainable robot. During the task, the robot provided suggestions and, depending on the experimental condition, explanations to justify those suggestions. We compared participants' behaviours in interacting with both types of robots. In particular, we investigated how participants' personality dimensions and previous experiences with the iCub robot impacted participants' decision-making. We also studied how participants aligned with iCub's playing style as the interaction continued. Our results show that participants' negative agency and agreeableness substantially impacted how they accepted the robot's suggestions when it provided example-based counterfactual explanations. We also observed a learning effect: participants tended to align with the robot's playing style during the interaction. However, the participants' learning depended not only on the presence of the explanations, but also on the time spent with the robot. Moreover, the human-robot team's victories were mainly attributable to the robot's persuasiveness rather than the participants' skills in the game.</p>
Perception is Only Real When Shared: A Mathematical Model for Collaborative Shared Perception in Human-Robot Interaction
No full textPartners have to build a shared understanding of their environment in everyday collaborative tasks by aligning their perceptions and establishing a common ground. This is one of the aims of shared perception: revealing characteristics of the individual perception to others with whom we share the same environment. In this regard, social cognitive processes, such as joint attention and perspective-taking, form a shared perception. From a Human-Robot Interaction (HRI) perspective, robots would benefit from the ability to establish shared perception with humans and a common understanding of the environment with their partners. In this work, we wanted to assess whether a robot, considering the differences in perception between itself and its partner, could be more effective in its helping role and to what extent this improves task completion and the interaction experience. For this purpose, we designed a mathematical model for a collaborative shared perception that aims to maximise the collaborators’ knowledge of the environment when there are asymmetries in perception. Moreover, we instantiated and tested our model via a real HRI scenario. The experiment consisted of a cooperative game in which participants had to build towers of Lego bricks, while the robot took the role of a suggester. In particular, we conducted experiments using two different robot behaviours. In one condition, based on shared perception, the robot gave suggestions by considering the partners’ point of view and using its inference about their common ground to select the most informative hint. In the other condition, the robot just indicated the brick that would have yielded a higher score from its individual perspective. The adoption of shared perception in the selection of suggestions led to better performances in all the instances of the game where the visual information was not a priori common to both agents. However, the subjective evaluation of the robot’s behaviour did not change between conditions
Intra and post-operative bleeding and healing in patients using anticoagulant therapy: a large cohort study.
No full textRelationship between periodontal biotype and dental malocclusion: a longitudinal cohort study
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