We study hybrid execution in multi-agent reinforcement learning (MARL), a paradigm where agents aim to complete cooperative tasks with arbitrary communication levels at execution time by taking advantage of information-sharing among the agents. Under hybrid execution, the communication level can range from a setting in which no communication is allowed between agents (fully decentralized), to a setting featuring full communication (fully centralized), but the agents do not know beforehand which communication level they will encounter at execution time. We contribute MARO, an approach that makes use of an auto-regressive predictive model, trained in a centralized manner, to estimate missing agents' observations at execution time. We evaluate MARO on standard scenarios and extensions of previous benchmarks tailored to emphasize the impact of partial observability in MARL. Experimental results show that our method consistently outperforms relevant baselines, allowing agents to act with faulty communication while successfully exploiting shared information.

Social robots can support children’s emotional skills development through playful interactions, yet skills like emotional self-disclosure remain underexplored. This study investigates the impact of a social robot designed to encourage emotional self-disclosure during an emotion-identification game with children aged 6-10. In a between-subjects design with 28 participants across two local schools, we compared a Reflective condition, where the robot actively encouraged emotional self-disclosure through question-asking and reciprocal sharing, to a Control condition, where the robot did not. Children in the Reflective condition engaged in emotional self-disclosure when prompted, and showed higher engagement than those in Control. Direct question-asking was more effective than reciprocal self-disclosure. Results suggested that children who perceived the robot as kinder disclosed more, whereas those who viewed it as more real disclosed less. These findings highlight the potential of social robots to foster emotional skills in children and inform the design of future child-robot interaction research.

In the era of artificial intelligence, AI-assisted decision-making has become a common paradigm.Explainable Artificial Intelligence has been one of the more explored factors in improving transparency of AI tools in AI-assisted decision-making, but sometimes with contradictory results.Furthermore, while individual AI-assisted decisionmaking has garnered substantial investigation, the domain of group AI-assisted decision-making remains notably underexplored.This research presents the first look at the impact of explainability and team composition on AI-assisted decision-making.With a controlled experiment on mushroom edibility classification, with 89 participants, we show that the impact of XAI is more pronounced in decision-making with groups (2-person) than in individual decisionmaking.Groups rely less on incorrect AI recommendations when explanations are available, but they rely more on incorrect AI recommendations when explanations are absent, compared to individual decision makers.This phenomenon underscores the amplified effect of explainability in AI-assisted decision-making in group settings.

Although research often explores how human-robot interaction influences cooperation in social dilemma scenarios such as the Public Goods Game, most research focuses on robots taking active roles in the game i.e. opponents or team-players. Considering the potential of assistant robots to influence human decision-making, this study explores how an assistant robot with prosocial or individualistic goals influences cooperation in a Public Goods Game. In a between-subjects study (N=60), participants interacted with the robot in one of three conditions: Prosocial, where the robot supported cooperative behavior; Individualistic, where it encouraged self-serving actions; and Control, where the robot provided feedback on the game state without expressing individual goals. Results revealed that participants in the Prosocial or Individualistic conditions contributed less to the public good compared to those in the Control condition. The Prosocial and Individualistic robots were perceived as warmer but evoking more discomfort compared to the Control. Notably, participants who played with the Prosocial robot reported increased trust, not only in the robot but also in their fellow players. These findings suggest that alignment between an assistant robot’s goals and expected social norms plays a key role in trust perception and it also shapes group dynamics. We discuss important considerations for designing assistant robots that provide moral recommendations.

We study hybrid execution in multi-agent reinforcement learning (MARL), a paradigm where agents aim to complete cooperative tasks with arbitrary communication levels at execution time by taking advantage of information-sharing among the agents. Under hybrid execution, the communication level can range from a setting in which no communication is allowed between agents (fully decentralized), to a setting featuring full communication (fully centralized), but the agents do not know beforehand which communication level they will encounter at execution time. We contribute MARO, an approach that makes use of an auto-regressive predictive model, trained in a centralized manner, to estimate missing agents' observations at execution time. We evaluate MARO on standard scenarios and extensions of previous benchmarks tailored to emphasize the impact of partial observability in MARL. Experimental results show that our method consistently outperforms relevant baselines, allowing agents to act with faulty communication while successfully exploiting shared information.

There is an increasing interest in Human–computer Interaction in multisensory interactive systems, creating a need to deepen our understanding of how multiple sensory modalities relate to and affect each other. We extend prior research on crossmodal correspondences by investigating colour and emotional associations with pure vibrotactile stimuli. We asked 32 participants to assign colour properties (hue and brightness) and emotional categories (pleasure, arousal, and dominance) to stimuli of varying amplitude and angular frequency . We found that perceptions of pleasure and arousal increased with amplitude and angular frequency, but negative and relaxing emotional states were not able to convey. We also found associations between vibrotactile stimuli and colour properties. High amplitude stimuli were linked to warm colours and darker shades, while low amplitude was associated with brighter shades and greater variance in hue. We finish by discussing the causal mechanisms of crossmodal correspondences and contribute a design space for creating multisensory experiences.

Shape-changing skin is an exciting modality due to its accessible and engaging nature. Its softness and flexibility make it adaptable to different interactive devices that children with and without visual impairments can share. Although their potential as an emotionally expressive medium has been shown for sighted adults, their potential as an inclusive modality remains unexplored. This work explores the shape-emotional mappings in children with and without visual impairment. We conducted a user study with 50 children (26 with visual impairment) to investigate their emotional associations with five skin shapes and two movement conditions. Results show that shape-emotional mappings are dependent on visual abilities. Our study raises awareness of the influence of visual experiences on tactile vocabulary and emotional mapping among sighted, low-vision, and blind children. We finish discussing the causal associations between tactile stimuli and emotions and suggest inclusive design recommendations for shape-changing devices.

Understanding the conundrum of human cooperation has been declared one of the century’s grand challenges. Traditionally, the evolution of cooperative action in nature is analyzed through the lens of Evolutionary Game Theory, specifically, using the social learning framework, a model for Darwinian competition. However, more complex individuals may resort to more sophisticated learning rules such as Counterfactual Thinking (CT). Given these individuals’ cognitive empowerment, the question of how the presence of counterfactuals influences the evolution of cooperation in a hybrid population of these complex agents and social learners. Here we explore how cooperation emerges from the interplay of different strategy revision paradigms by analyzing large-scale Markov processes. We find that increasing the prevalence of CT individuals can promote cooperation, but such an increase is non-monotonous. Moreover, whereas counterfactual reasoning generally fosters cooperation, it fails to promote such behaviour among counterfactuals. Lastly, we find that increasing the population’s heterogeneity level enhances cooperation among social learners, but again not among counterfactuals. This indicates that, under certain circumstances, the presence of more sophisticated agents may help promote cooperation in hybrid populations. The proposed study may come as a starting point for a more profound understanding of agents’ counterfactual rationality impact on hybrid populations.

Research on robotic ostracism is still scarce and has only explored its effects on adult populations. Although the results revealed important carryover effects of robotic exclusion, there is no evidence yet that those results occur in child-robot interactions. This paper provides the first exploration of robotic ostracism with children. We conducted a study using the Robotic Cyberball Paradigm in a third-person perspective with a sample of 52 children aged between five to ten years old. The experimental study had two conditions: Exclusion and Inclusion. In the Exclusion condition, children observed a peer being excluded by two robots; while in the Inclusion condition, the observed peer interacted equally with the robots. Notably, even 5-year-old children could discern when robots excluded another child. Children who observed exclusion reported lower levels of belonging and control, and exhibited higher prosocial behaviour than those witnessing inclusion. However, no differences were found in children's meaningful existence, self-esteem, and physical proximity across conditions. Our user study provides important methodological considerations for applying the Robotic Cyberball Paradigm with children. The results extend previous literature on both robotic ostracism with adults and interpersonal ostracism with children. We finish discussing the broader implications of children observing ostracism in human-robot interactions.

Children with visual impairments often struggle to fully participate in group activities due to limited access to visual cues. They have difficulty perceiving what is happening, when, and how to act-leading to children with and without visual impairments being frustrated with the group activity, reducing mutual interactions. To address this, we created Touchibo, a tactile storyteller robot acting in a multisensory setting, encouraging touch-based interactions. Touchibo provides an inclusive space for group interaction as touch is a highly accessible modality in a mixed-visual ability context. In a study involving 107 children (37 with visual impairments), we compared Touchibo to an audio-only storyteller. Results indicate that Touchibo significantly improved children's individual and group participation perception, sparking touch-based interactions and the storyteller was more likable and helpful. Our study highlights touch-based robots' potential to enrich children's social interactions by prompting interpersonal touch, particularly in mixed-visual ability settings.

Spatial reasoning is one of the malleable skills well-suited to be developed using robotics that not only benefits children in their pursuit of STEM-related topics but also fosters their perspective-taking skills on dimensions beyond spatial skills. In a study involving elementary school children aged 7-10 years old, we investigated the impact of playing a game with physical robots in a physical environment versus playing the same game with virtually embodied robots in a virtual environment. The game focused on developing spatial perspective-taking skills, requiring children to make moves based on the robots’ point of view. We examined how the two environments influenced their experience of fun and learning spatial reasoning skills. We conducted a between-subject user study with 59 participants from 3rd and 4th grades, where they either played with the physical or virtual version of the game. Children in both conditions showed significant improvement in their perspective-taking and spatial orientation test scores. Furthermore, they rated the physical game as more fun compared to the virtual version.

Inclusion is key in group work and collaborative learning. We developed a mediator robot to support and promote inclusion in group conversations, particularly in groups composed of children with and without visual impairment. We investigate the effect of two mediation strategies on group dynamics, inclusion, and perception of the robot. We conducted a within-subjects study with 78 children, 26 experienced visual impairments, in a decision-making activity. Results indicate that the robot can foster inclusion in mixed-visual ability group conversations. The robot succeeds in balancing participation, particularly when using a highly intervening mediating strategy (directive strategy). However, children feel more heard by their peers when the robot is less intervening (organic strategy). We extend prior work on social robots to assist group work and contribute with a mediator robot that enables children with visual impairments to engage equally in group conversations. We finish by discussing design implications for inclusive social robots.

Spatial understanding and communication are essential skills in human interaction. An adequate understanding of others’ spatial perspectives can increase the quality of the interaction, both perceptually and cognitively. In this paper, we take the first step towards understanding children’s perspective-taking abilities and their tendency to adapt their perspective to a counterpart while completing a task with a robot. The elements used for studying children’s behaviours are the frame of reference and perspective marking, which we evaluated through a task where players needed to compose instructions to guide each other to complete the task. We developed the interaction with an NAO robot and analyzed the children’s instructions and their performance throughout the game. Our initial findings demonstrated that children tend to compose their first instruction by following the principle of least collaborative effort. Children significantly changed and adapted their perspective, i.e. frame of reference and perspective marking to the robot, mainly when the robot failed to follow their instructions correctly. Additionally, results show that children tend to create a mental model of their counterparts and the robot changing that frame of reference might affect their performance or the flow of the interaction.

Storytelling has the potential to be an inclusive and collaborative activity. However, it is unclear how interactive storytelling systems can support such activities, particularly when considering mixed-visual ability children. In this paper, we present an interactive multisensory storytelling system and explore the extent to which an emotional robot can be used to support inclusive experiences. We investigate the effect of the robot's emotional behavior on the joint storytelling process, resulting narratives, and collaboration dynamics. Results show that when children co-create stories with a robot that exhibits emotional behaviors, they include more emotive elements in their stories and explicitly accept more ideas from their peers. We contribute with a multisensory environment that enables children with visual impairments to engage in joint storytelling activities with their peers and analyze the effect of a robot's emotional behaviors on an inclusive storytelling experience.

While creativity has been previously studied in Child-Robot Interaction (cHRI), the effect of regulatory focus on creativity skills has not been investigated. This paper presents an exploratory study that, for the first time, uses the Regulatory Focus Theory (RFT) to assess children's creativity skills in an educational context with a social robot. We investigated whether two key emotional regulation techniques, promotion (approach) and prevention (avoidance), stimulate creativity during a story-telling activity between a child and a robot. We conducted a between-subjects field study with 69 children between the ages of 7 and 9 years old, divided between two study conditions: (1) promotion, where a social robot primes children for action by eliciting positive emotional states, and (2) prevention, where a social robot primes children for avoidance by evoking a states related to security and safety associated with blockage-oriented behaviors. To assess changes in creativity as a response to the priming interaction, children were asked to tell stories to the robot before (pre-test) and after (post-test) the priming interaction. We measured creativity levels by analyzing the verbal content of the stories. We coded verbal expressions related to creativity variables, including fluency, flexibility, elaboration, and originality. Our results show that children in the promotion condition generated significantly more ideas, and their ideas were on average more original in the stories they created in the post-test rather than in the pre-test. We also modeled the process of creativity that emerges during storytelling in response to the robot's verbal behavior. This paper enriches the scientific understanding of creativity emergence in child-robot collaborative interactions.

Learning representations of multimodal data that are both informative and robust to missing modalities at test time remains a challenging problem due to the inherent heterogeneity of data obtained from different channels. To address it, we present a novel Geometric Multimodal Contrastive (GMC) representation learning method consisting of two main components: i) a two-level architecture consisting of modality-specific base encoders, allowing to process an arbitrary number of modalities to an intermediate representation of fixed dimensionality, and a shared projection head, mapping the intermediate representations to a latent representation space; ii) a multimodal contrastive loss function that encourages the geometric alignment of the learned representations. We experimentally demonstrate that GMC representations are semantically rich and achieve state-of-the-art performance with missing modality information on three different learning problems including prediction and reinforcement learning tasks.

Throughout the past few years, artificial intelligence (AI) has become increasingly more present in our daily lives. A myriad of settings became the stage for AI applications, such as factories, roads, houses, hospitals and even schools. Given these new contexts, AI-powered machines must now place the human at the centre, and be designed to interact with humans in a natural way: AI is becoming social.

Recent studies with children have pointed out the importance of spatial thinking as an essential factor in determining later success in STEM-related fields. The current study explores the potential of using embodied activities with robots to aid the development of children’s spatial perspective-taking abilities. This research focuses on evaluating children’s spatial perspective-taking abilities and assessing the potential of the designed activity to practice perspective-taking. The activity design is inspired by the dynamic and mental processes involved in remote-controlled cars and racing games, it is developed with a Cozmo robot, and it includes guiding the robot within the maze by considering the robot’s point of view. We evaluated the activity through a user study with 22 elementary school children between the ages of 8 and 9. The findings showed that children’s performance at different angular disparities was aligned with the previous research in developmental psychology. Additionally, most children made fewer mistakes in guiding the robot as they played more. Finally, while we did not observe any performance improvement in the group of children who had access to the robot’s point of view during the game, we learned new insights about how children perceived seeing the maze through the robot’s eyes.

As routinely working with robots spreads globally, it becomes important to understand how best to customize robots to each culture to maximize collaboration between humans and robots. In two distinct cultures (United States and Portugal) we examined group-based emotions toward robots with participants self-categorizing three different ways (ingroup, outgroup, and neutral). We tested and confirmed our baseline assumptions that Portuguese participants are more collectivistic and less individualistic, and feel closer with a team in negative, but not positive, scenarios, compared to United States participants. Supporting our hypotheses, the results showed that participants rated more positive emotions toward the robot in the ingroup condition than in the outgroup or neutral conditions. Moreover, an interaction effect between culture and self-categorization revealed that Portuguese participants had more positive group-based emotions toward the robot than United States participants when self-categorizing as an ingroup. We discuss the implications in terms of human-robot teaming and potential future research directions.

More than a decade has passed since the development of FearNot!, an application designed to help children deal with bullying through role-playing with virtual characters. It was also the application that led to the creation of FAtiMA, an affective agent architecture for creating autonomous characters that can evoke empathic responses. In this article, we describe the FAtiMA Toolkit, a collection of open-source tools that is designed to help researchers, game developers, and roboticists incorporate a computational model of emotion and decision-making in their work. The toolkit was developed with the goal of making FAtiMA more accessible, easier to incorporate into different projects, and more flexible in its capabilities for human-agent interaction, based upon the experience gathered over the years across different virtual environments and human-robot interaction scenarios. As a result, this work makes several different contributions to the field of Agent-Based Architectures. More precisely, the FAtiMA Toolkit’s library-based design allows developers to easily integrate it with other frameworks, its meta-cognitive model affords different internal reasoners and affective components, and its explicit dialogue structure gives control to the author even within highly complex scenarios. To demonstrate the use of the FAtiMA Toolkit, several different use cases where the toolkit was successfully applied are described and discussed.

Robots have massively been introduced in children’s lives, showing promising effects on education and learning. Parallel to this, children’s creative levels show a decline related to different factors, including the standardized teaching and learning dynamics present in traditional school systems. This work aims to investigate if the activities with robots already present in schools affect children’s creativity levels. To study this, we compared creative levels of children across three study conditions: (1) Experimental condition 1: Children performed STEM activities in school by learning how to program robots; (2) Experimental condition 2: Children performed STEAM activities by learning how to design robots; (3) Control condition: Children engaged in a music class. We applied the Test for Creative Thinking-Drawing Production (TCT-DP), a validated test that measures creative potential, before and after the intervention. Our results showed that the creativity levels of children increased from pre- to post-testing, revealing the effect of all intervention groups in potentiating creativity. Additionally, results showed that creative levels were significantly higher in the control condition. This result was expected since this condition consisted of an artistic musical intervention where creativity is foreseen to be stimulated. When analyzing the effects of the interventions on the two dimensions of TCT-DP (i.e., adaptiveness and innovativeness), results showed that both the control and the programming condition stimulated innovativeness. This result seems to show that STEM activities can stimulate non-conventional ways of thinking, similarly to creative activities such as a music class. While much has been studied about how STEM activities influence the knowledge of children, little is known if STEM also contributes to the stimulation of their creativity. This study promotes investigation in this topic and shows the potential of using robots to unlock creative potentials in children.

According to psychology research, emotional induction has positive implications in many domains such as therapy and education. Our aim in this paper was to manipulate the Regulatory Focus Theory to assess its impact on the induction of regulatory focus related emotions in children in a pretend play scenario with a social robot. The Regulatory Focus Theory suggests that people follow one of two paradigms while attempting to achieve a goal; by seeking gains (promotion focus - associated with feelings of happiness) or by avoiding losses (prevention focus - associated with feelings of fear). We conducted a study with 69 school children in two different conditions (promotion vs. prevention). We succeeded in inducing happiness emotions in the promotion condition and found a resulting positive effect of the induction on children’s social engagement with the robot. We also discuss the important implications of these results in both educational and child robot interaction fields.

Visually impaired children (VI) face challenges in collaborative learning in classrooms. Robots have the potential to support inclusive classroom experiences by leveraging their physicality, bespoke social behaviors, sensors, and multimodal feedback. However, the design of social robots for mixed-visual abilities classrooms remains mostly unexplored. This paper presents a four-month-long community-based design process where we engaged with a school community. We provide insights into the barriers experienced by children and how social robots can address them. We also report on a participatory design activity with mixed-visual abilities children, highlighting the expected roles, attitudes, and physical characteristics of robots. Findings contextualize social robots within inclusive classroom settings as a holistic solution that can interact anywhere when needed and suggest a broader view of inclusion beyond disability. These include children’s personality traits, technology access, and mastery of school subjects. We finish by providing reflections on the community-based design process.

We present the design process of the robot YOLO aimed at stimulating creativity in children. This robot was developed under a human-centered design approach with participatory design practices during two years and involving 142 children as active contributors at all design stages. The main contribution of this work is the development of methods and tools for child-centered robot design. We adapted existing participatory design practices used with adults to fit children's development stages. We followed the Double-Diamond Design Process Model and rested the design process of the robot on the following principles: low floor and wide walls, creativity provocations, open-ended playfulness, and disappointment avoidance through abstraction. The final product is a social robot designed for and with children. Our results show that YOLO increases their creativity during play, demonstrating a successful robot design project. We identified several guidelines that made the design process successful: the use of toys as tools, playgrounds as spaces, the emphasis of playfulness for child expression, and child policies as allies for design studies. The design process described empowers children's in the design of robots.

In this work we explore implicit communication between humans and robots—through movement—in multi-party (or multi-user) interactions. In particular, we investigate how a robot can move to better convey its intentions using legible movements in multi-party interactions. Current research on the application of legible movements has focused on single-user interactions, causing a vacuum of knowledge regarding the impact of such movements in multi-party interactions. We propose a novel approach that extends the notion of legible motion to multi-party settings, by considering that legibility depends on all human users involved in the interaction, and should take into consideration how each of them perceives the robot’s movements from their respective points-of-view. We show, through simulation and a user study, that our proposed model of multi-user legibility leads to movements that, on average, optimize the legibility of the motion as perceived by the group of users. Our model creates movements that allow each human to more quickly and confidently understand what are the robot’s intentions, thus creating safer, clearer and more efficient interactions and collaborations.

The issue of how to make embodied agents explainable has experienced a surge of interest over the past 3 years, and there are many terms that refer to this concept, such as transparency and legibility. One reason for this high variance in terminology is the unique array of social cues that embodied agents can access in contrast to that accessed by non-embodied agents. Another reason is that different authors use these terms in different ways. Hence, we review the existing literature on explainability and organize it by (1) providing an overview of existing definitions, (2) showing how explainability is implemented and how it exploits different social cues, and (3) showing how the impact of explainability is measured. Additionally, we present a list of open questions and challenges that highlight areas that require further investigation by the community. This provides the interested reader with an overview of the current state of the art.

Geometry and handwriting rely heavily on the visual representation of basic shapes. It can become challenging for students with visual impairments to perceive these shapes and understand complex spatial constructs. For instance, knowing how to draw is highly dependent on spatial and temporal components, which are often inaccessible to children with visual impairments. Hand-held robots, such as the Cellulo robots, open unique opportunities to teach drawing and writing through haptic feedback. In this paper, we investigate how these tangible robots could support inclusive, collaborative learning activities, particularly for children with visual impairments. We conducted a user study with 20 pupils with and without visual impairments, where they engaged in multiple drawing activities with tangible robots. We contribute novel insights on the design of children-robot interaction, learning shapes and letters, children engagement, and responses in a collaborative scenario that address the challenges of inclusive learning.

The behaviour of a person in a given situation can be explained understanding his personality traits. In this sense, the identification of these traits can be a great value to achieve personalised social influence. Although there are several models of persuasion, few of them take into account the person's personality traits. For this reason, this work describes a persuasion study that takes into account a person's personality. We develop a storytelling decision-making scenario, where the participant receives influencing messages to follow a pattern of behaviour determined by a persuasive agent (an autonomous social robot with assertive behaviour). From the study, we find evidence that the model used within the proposed scenario managed to make participants more engaged in the activity. We found pieces of evidence that the levels of assertiveness of a person can influence their attitude and perception by an agent. Also, we identify that persuasion strategy which uses persuasive arguments are more efficient than strategies that have no arguments. Finally, our proposed persuasion strategies have achieved a good level of successful influence.

Creativity is an intrinsic human ability with multiple benefits across lifespan. Despite its importance, societies not always are well equipped to provide contexts for creativity stimulation; as a consequence, a major decline in creative abilities occurs at the age of 7 years old. In this paper, we investigated the effectiveness of using a robotic system named YOLO, as an intervention tool to stimulate creativity in children. During the intervention, children used YOLO as a character for their stories and through the interaction with the robot, creative abilities were stimulated. Our study (n = 62) included 3 experimental conditions: i) YOLO displayed behaviors based on creativity techniques; ii) YOLO displayed behaviors based on creativity techniques plus social behaviors; iii) YOLO was turned off, not displaying any behaviors. We measured children's creative abilities at pre and posttesting and their creative process through behavior analysis. Results showed that the interaction with YOLO contributed to higher creativity levels in children, specifically contributing to the generation of more original ideas during story creation. This study shows the potential of using social robots as tools to empower intrinsic human abilities, such as the ability of being creative.

Future human-robot interactions will have to consider different human traits. One human feature that may be affected by the presence of virtual agents or robots is human honesty. Will people try to take advantage in the presence of a robot/virtual agent? Some previous studies have shown that the physical presence of a robot can decrease cheating in humans. In this paper, we investigated if merely a simple video of a robot looking at the user was enough to affect human's cheating behavior. Further, we also investigated if the Honesty-Humility personality trait predicted cheating. We conducted a study with 160 participants that were randomly allocated to one of two conditions: (1) performing the task with a video of a robot looking at them, or (2) doing the task alone. Results showed that being alone or with a video of a robot produced equal levels of cheating and the Honesty-Humility dimension predicted cheating, particularly the fairness sub-domain was responsible for predicting cheating behavior. This study has implications for future scenarios where dishonesty might be tempting, and physical presence of an observer might not be possible.

This paper presents the results of a user study designed to investigate social robots' persuasiveness. In the design, the robot attempts to persuade users in two different conditions comparing to a control condition. In one condition, the robot aims at persuading users by giving them a reward. In the second condition, the robot tries to persuade by punishing users. The results indicated that the robot succeeded to persuade the users to select a less-desirable choice comparing to a better one. However, no difference was found in the perception of the robot's warmth nor discomfort, comparing the two strategies. The results suggest that social robots are capable of persuading users objectively, but further investigation is required to investigate persuasion subjectively.

Abstract Understanding how to design agents that sustain cooperation in multi-agent systems has been a long-lasting goal in distributed artificial intelligence. Proposed solutions rely on identifying free-riders and avoiding cooperating or interacting with them. These mechanisms of social control are traditionally studied in games with linear and deterministic payoffs, such as the prisoner’s dilemma or the public goods game. In reality, however, agents often face dilemmas in which payoffs are uncertain and non-linear, as collective success requires a minimum number of cooperators. The collective risk dilemma (CRD) is one of these games, and it is unclear whether the known mechanisms of cooperation remain effective in this case. Here we study the emergence of cooperation in CRD through partner-based selection. First, we discuss an experiment in which groups of humans and robots play a CRD. This experiment suggests that people only prefer cooperative partners when they lose a previous game (i.e., when collective success was not previously achieved). Secondly, we develop an evolutionary game theoretical model pointing out the evolutionary advantages of preferring cooperative partners only when a previous game was lost. We show that this strategy constitutes a favorable balance between strictness (only interact with cooperators) and softness (cooperate and interact with everyone), thus suggesting a new way of designing agents that promote cooperation in CRD. We confirm these theoretical results through computer simulations considering a more complex strategy space. Third, resorting to online human–agent experiments, we observe that participants are more likely to accept playing in a group with one defector when they won in a previous CRD, when compared to participants that lost the game. These empirical results provide additional support to the human predisposition to use outcome-based partner selection strategies in human–agent interactions.

Bystander behaviour and self-efficacy beliefs play an important role in cyberbullying incidence. This study tested the relationship between the Bystander Intervention Model phases and the mediating role of adolescents’ self-efficacy beliefs. Children from the fifth to twelfth grade (N = 676) participated in this study by responding to questionnaires concerning the various phases of the Bystander Intervention Model and self-efficacy beliefs to resolve cyberbullying-related problems. Through structural equation modelling, noticing an incident of cyberbullying had a direct and indirect effect on aggressive behaviour, and an indirect effect on reporting and problem-solving behaviour. The indirect effect of interpreting the event through attributing responsibility was significant for aggressive and problem-solving behaviour. The mediator role of reflective decision-making had a stronger effect on direct problem-solving. Self-efficacy beliefs significantly affected the relationship between interpreting the event and all behaviour, but stronger for direct problem-solving. These findings help explain empirically how bystanders respond to incidents of cyberbullying.

Fairness plays a fundamental role in decision-making, which is evidenced by the high incidence of human behaviors that result in egalitarian outcomes. This is often shown in the context of dyadic interactions, resorting to the Ultimatum Game. The peculiarities of group interactions – and the corresponding effect in eliciting fair actions – remain, however, astray. Focusing on groups suggests several questions related with the effect of group size, group decision rules and the interrelation of human and agents’ behaviors in hybrid groups. To address these topics, here we test a Multiplayer version of the Ultimatum Game (MUG): proposals are made to groups of Responders that, collectively, accept or reject them. Firstly, we run an online experiment to evaluate how humans react to different group decision rules. We observe that people become increasingly fair if groups adopt stricter decision rules, i.e., if more individuals are required to accept a proposal for it to be accepted by the group. Secondly, we propose a new analytical model to shed light on how such behaviors may have evolved. Thirdly, we adapt our model to include agents with fixed behaviors. We show that including hardcoded Pro-social agents favors the evolutionary stability of fair states, even for soft group decision rules. This suggests that judiciously introducing agents with particular behaviors in a population may leverage long-term social benefits.

This paper explores the role of prosocial behaviour when people team up with robots in a collaborative game that presents a social dilemma similar to a public goods game. An experiment was conducted with the proposed game in which each participant joined a team with a prosocial robot and a selfish robot. During 5 rounds of the game, each player chooses between contributing to the team goal (cooperate) or contributing to his individual goal (defect). The prosociality level of the robots only affects their strategies to play the game, as one always cooperates and the other always defects. We conducted a user study at the office of a large corporation with 70 participants where we manipulated the game result (winning or losing) in a between-subjects design. Results revealed two important considerations: (1) the prosocial robot was rated more positively in terms of its social attributes than the selfish robot, regardless of the game result; (2) the perception of competence, the responsibility attribution (blame/credit), and the preference for a future partner revealed significant differences only in the losing condition. These results yield important concerns for the creation of robotic partners, the understanding of group dynamics and, from a more general perspective, the promotion of a prosocial society.

This PhD project aims at investigating how a social robot can adapt its behaviours to the group members in order to achieve more positive group dynamics, which we identify as group intelligence. This goal is supported by our previous work, which contains relevant data and insightful results to the understanding of group interactions between humans and robots. Finally, we examine and discuss the future work we have planned and what are the contributions to Human-Robot Interaction (HRI) field.

Our goal is to disseminate an exploratory investigation that examined how physical presence and collaboration can be important factors in the development of assistive robots that can go beyond information-giving technologies. In particular, this video exhibits the setting and procedures of a user study that explored different types of collaborative interactions between robots and blind people.

In this paper we sought to understand how the display of different levels of warmth and competence, as well as, different roles (opponent versus partner) portrayed by a robot, affect the display of emotional responses towards robots and how they can be used to predict future intention to work. For this purpose we devised an entertainment card-game group scenario involving two humans and two robots (n=54). The results suggest that different levels of warmth and competence are associated with distinct emotional responses from users and that these variables are useful in predicting future intention to work, thus hinting at the importance of considering warmth and competence stereotypes in Human-Robot Interaction.

In this paper, we propose a novel setting for Inverse Reinforcement Learning (IRL), namely "Learning from a Learner" (LfL). As opposed to standard IRL, it does not consist in learning a reward by observing an optimal agent but from observations of another learning (and thus sub-optimal) agent. To do so, we leverage the fact that the observed agent’s policy is assumed to improve over time. The ultimate goal of this approach is to recover the actual environment’s reward and to allow the observer to outperform the learner. To recover that reward in practice, we propose methods based on the entropy-regularized policy iteration framework. We discuss different approaches to learn solely from trajectories in the state-action space. We demonstrate the genericity of our method by observing agents implementing various reinforcement learning algorithms. Finally, we show that, on both discrete and continuous state/action tasks, the observer’s performance (that optimizes the recovered reward) can surpass those of the observed agent.

People are not perfect, and if given the chance, some will be dishonest with no regrets. Some people will cheat just a little to gain some advantage, and others will not do it at all. With the prospect of more human-robot interactions in the future, it will become very important to understand which kind of roles a robot can have in the regulation of cheating behavior. We investigated whether people will cheat while in the presence of a robot and to what extent this depends on the role the robot plays. We ran a study to test cheating behavior with a die task, and allocated people to one of the following conditions: 1) participants were alone in the room while doing the task; 2) with a robot with a vigilant role or 3) with a robot that had a supporting role in the task, accompanying and giving instructions. Our results showed that participants cheated significantly more than chance when they were alone or with the robot giving instructions. In contrast, cheating could not be proven when the robot presented a vigilant role. This study has implications for human-robot interaction and for the deployment of autonomous robots in sensitive roles in which people may be prone to dishonest behavior.

Humans interact in rich and diverse ways with the environment. However, the representation of such behavior by artificial agents is often limited. In this work we present motion concepts, a novel multimodal representation of human actions in a household environment. A motion concept encompasses a probabilistic description of the kinematics of the action along with its contextual background, namely the location and the objects held during the performance. We introduce a novel algorithm which learns and recognizes motion concepts from action demonstrations, named Online Motion Concept Learning (OMCL). The algorithm is evaluated on a virtual-reality household environment with the presence of a human avatar. OMCL outperforms standard motion recognition algorithms on an one-shot recognition task, attesting to its potential for sample-efficient recognition of human actions.

This paper explores how robotic teammates can enhance and promote cooperation in collaborative settings. It presents a user study in which participants engaged with two fully autonomous robotic partners to play a game together, named “For The Record”, a variation of a public goods game. The game is played for a total of five rounds and in each of them, players face a social dilemma: to cooperate i.e., contributing towards the team's goal while compromising individual benefits, or to defect i.e., favouring individual benefits over the team's goal. Each participant collaborates with two robotic partners that adopt opposite strategies to play the game: one of them is an unconditional cooperator (the pro-social robot), and the other is an unconditional defector (the selfish robot). In a between-subjects design, we manipulated which of the two robots criticizes behaviours, which consists of condemning participants when they opt to defect, and it represents either an alignment or a misalignment of words and deeds by the robot. Two main findings should be highlighted (1) the misalignment of words and deeds may affect the level of discomfort perceived on a robotic partner; (2) the perception a human has of a robotic partner that criticizes him is not damaged as long as the robot displays an alignment of words and deeds.

Recent advances on Social Robotics raise the question whether a social robot can be used as a persuasive agent. To date, a body of literature has been performed using various approaches to answer this research question, ranging from the use of non-verbal behavior to the exploration of different embodiment characteristics. In this paper, we investigate the role of social power for making social robots more persuasive. Social power is defined as one's ability to influence another to do something which s/he would not do without the presence of such power. Different theories classify alternative ways to achieve social power, such as providing a reward, using coercion, or acting as an expert. In this work, we explored two types of persuasive strategies that are based on social power (specifically Reward and Expertise) and created two social robots that would employ such strategies. To examine the effectiveness of these strategies we performed a user study with 51 participants using two social robots in an adversarial setting in which both robots try to persuade the user on a concrete choice. The results show that even though each of the strategies caused the robots to be perceived differently in terms of their competence and warmth, both were similarly persuasive.

Trust mechanisms are considered the logical protection of software systems, preventing malicious people from taking advantage or cheating others. Although these concepts are widely used, most applications in this field do not consider affective aspects to aid in trust computation. Researchers of Psychology, Neurology, Anthropology, and Computer Science argue that affective aspects are essential to human's decision-making processes. So far, there is a lack of understanding about how these aspects impact user's trust, particularly when they are inserted in an evaluation system. In this paper, we propose a trust model that accounts for personality using three personality models: Big Five, Needs, and Values. We tested our approach by extracting personality aspects from texts provided by two online human-fed evaluation systems and correlating them to reputation values. The empirical experiments show statistically significant better results in comparison to non-personality-wise approaches.

This work explores a group learning scenario with an autonomous empathic robot. We address two research questions: (1) Can an autonomous robot designed with empathic competencies foster collaborative learning in a group context? (2) Can an empathic robot sustain positive educational outcomes in long-term collaborative learning interactions with groups of students? To answer these questions, we developed an autonomous robot with empathic competencies that is able to interact with a group of students in a learning activity about sustainable development. Two studies were conducted. The first study compares learning outcomes in children across three conditions: learning with an empathic robot; learning with a robot without empathic capabilities; and learning without a robot. The results show that the autonomous robot with empathy fosters meaningful discussions about sustainability, which is a learning outcome in sustainability education. The second study features groups of students who interact with the robot in a school classroom for 2 months. The long-term educational interaction did not seem to provide significant learning gains, although there was a change in game-actions to achieve more sustainability during game-play. This result reflects the need to perform more long-term research in the field of educational robots for group learning.

This paper envisions a future where autonomous agents are used to foster and support pro-social behavior in a hybrid society of humans and machines. Pro-social behavior occurs when people and agents perform costly actions that benefit others. Acts such as helping others voluntarily, donating to charity, providing informations or sharing resources, are all forms of pro-social behavior. We discuss two questions that challenge a purely utilitarian view of human decision making and contextualize its role in hybrid societies: i) What are the conditions and mechanisms that lead societies of agents and humans to be more pro-social? ii) How can we engineer autonomous entities (agents and robots) that lead to more altruistic and cooperative behaviors in a hybrid society? We propose using social simulations, game theory, population dynamics, and studies with people in virtual or real environments (with robots) where both agents and humans interact. This research will constitute the basis for establishing the foundations for the new field of Pro-social Computing, aiming at understanding, predicting and promoting pro-sociality among humans, through artificial agents and multiagent systems.

This paper describes research aimed at supporting children's reading practices using a robot designed to interact with children as their reading companion. We use a learning by teaching scenario in which the robot has a similar or lower reading level compared to children, and needs help and extra practice to develop its reading skills. The interaction is structured with robot reading to the child and sometimes making mistakes as the robot is considered to be in the learning phase. Child corrects the robot by giving it instant feedbacks. To understand what kind of behavior can be more constructive to the interaction especially in helping the child, we evaluated the effect of a deictic gesture, namely pointing on the child's ability to find reading mistakes made by the robot. We designed three types of mistakes corresponding to different levels of reading mastery. We tested our system in a within-subject experiment with 16 children. We split children into a high and low reading proficiency even-though they were all beginners. For the high reading proficiency group, we observed that pointing gestures were beneficial for recognizing some types of mistakes that the robot made. For the earlier stage group of readers pointing were helping to find mistakes that were raised upon a mismatch between text and illustrations. However, surprisingly, for this same group of children, the deictic gestures were disturbing in recognizing mismatches between text and meaning.

Blind people rely on sighted peers and different assistive technologies to accomplish everyday tasks. In this paper, we explore how assistive robots can go beyond information-giving assistive technologies (e.g., screen readers) by physically collaborating with blind people. We first conducted a set of focus groups to assess how blind people perceive and envision robots. Results showed that, albeit having stereotypical concerns, participants conceive the integration of assistive robots in a broad range of everyday life scenarios and are welcoming of this type of technology. In a second study, we asked blind participants to collaborate with two versions of a robot in a Tangram assembly task: one robot would only provide static verbal instructions whereas the other would physically collaborate with participants and adjust the feedback to their performance. Results showed that active collaboration had a major influence on the successful performance of the task. Participants also reported higher perceived warmth, competence and usefulness when interacting with the physically assistive robot. Overall, we provide preliminary results on the usefulness of assistive robots and the possible role these can hold in fostering a higher degree of autonomy for blind people.

Social robots are being used to create better educational scenarios, thereby fostering children»s learning. In the work presented here, we describe an autonomous social robot that was designed to enhance children»s handwriting skills. Exploiting the benefits of the learning-by-teaching method, the system provides a scenario in which a child acts as a teacher and corrects the handwriting difficulties of the robotic agent. To explore the children»s perception towards this social robot and the effect on their learning, we have conducted a multi-session study with children that compared two contrasting competencies in the robot: 'learning' vs 'non-learning' and presented as two conditions in the study. The results suggest that the children learned more in the learning condition compared with the non-learning condition and their learning gains seem to be affected by their perception of the robot. The results did not lead to any significant differences in the children»s perception of the robot in the first two weeks of interaction. However, by the end of the 4th week, the results changed. The children in the learning condition gave significantly higher writing ability and overall performance scores to the robot compared with the non-learning condition. In addition, the change in the robot»s learning capabilities did not show to affect their perceived intelligence, likability and friendliness towards it.

Providing social robots an internal model of emotions can help them guide their behaviour in a more humane manner by simulating the ability to feel empathy towards others. Furthermore, the growing interest in creating robots that are capable of collaborating with other humans in team settings provides an opportunity to explore another side of human emotion, namely, group-based emotions. This paper contributes with the first model on group-based emotions in social robotic partners. We defined a model of group-based emotions for social robots that allowed us to create two distinct robotic characters that express either individual or group-based emotions. This paper also contributes with a user study where two autonomous robots embedded the previous characters, and formed two human-robot teams to play a competitive game. Our results showed that participants perceived the robot that expresses group-based emotions as more likeable and attributed higher levels of group identification and group trust towards their teams, when compared to the robotic partner that expresses individual-based emotions.

This study investigated non-verbal behavior and socioemotional interactions in small-groups of humans and robots. Sixty-participants were involved in a group setting in which they were required to play a card game with another human and two robots (playing as partners or as opponents). The two robots displayed different goal orientations: a competitive robot (named Emys-) and a relationship-driven cooperative robot (named Glin+). Video recordings of the interactions were analyzed in three game play sessions. Eye gaze and socioemotional support behaviors were coded based on Bales» Interaction Process Analysis. Results indicated that gaze behavior towards partners was more frequently displayed to the relationship-driven robot than to the competitive robot and the human partners. In contrast, gaze towards opponents occurred more often towards the competitive robot than to the relationship-driven robot and the human opponents. Socioemotional support occurred more frequently towards partners than opponents, and was also displayed more often towards humans than towards robots. Moreover, in the sessions where the robots were opponents, participants provided more support to the competitive robot. This investigation in small groups of humans and robots provided evidence of different interaction patterns towards robots displaying distinct orientation goals, which can be useful in guiding the successful design of social robots.

In this paper, we study the influence of a handshake in the human emotional bond to a robot. In particular, we evaluate the human willingness to help a robot whether the robot first introduces itself to the human with or without a handshake. In the tested paradigm the robot and the human have to perform a joint task, but at a certain stage, the robot needs help to navigate through an obstacle. Without requesting explicit help from the human, the robot performs some attempts to navigate through the obstacle, suggesting to the human that it requires help. In a study with 45 participants, we measure the human's perceptions of the social robot Vizzy, comparing the handshake vs non-handshake conditions. In addition, we evaluate the influence of a handshake in the pro-social behaviour of helping it and the willingness to help it in the future. The results show that a handshake increases the perception of Warmth, Animacy, Likeability, and the tendency to help the robot more, by removing the obstacle.

As robots begin to integrate our world and invade our streets and homes, they must act as autonomous and intelligent beings. However, so far, they are deprived of our responsive and emotional capacities, lacking awareness of the social world we live in. In the future, robots should be able to take into account these distinctive dimensions of human social interactions to be able to act appropriately within such social contexts. To do this, they must adapt and embody the essence of social and emotional intelligence. This not only includes the ability to recognise human emotions and social interactions but also understand them, deliberate them and act accordingly. Lately, significant research has been carried out in an attempt to find ways to build social and emotional robots that are able to perceive the user's emotions, adapt to them, and react appropriately. This talk will, therefore, provide an overview of the area of emotions in social interactions established between humans and social robots. In this analysis, I will use scenarios from educational and entertainment robotics, outline the process of building emotional social robots and finally proceed to interpret the effect that such capabilities have on user's interactions, learning, motivation, relationship and trust. I believe that by studying and engineering emotional and social interactions "for" and "with" robots, we have the opportunity to build a new generation of natural, engaging, effective and, most importantly, "humane" AI.

We contribute a learning from demonstration approach for robots to acquire skills from multi-modal high-dimensional data. Both latent representations and associations of different modalities are proposed to be jointly learned through an adapted variational auto-encoder. The implementation and results are demonstrated in a robotic handwriting scenario, where the visual sensory input and the arm joint writing motion are learned and coupled. We show the latent representations successfully construct a task manifold for the observed sensor modalities. Moreover, the learned associations can be exploited to directly synthesize arm joint handwriting motion from an image input in an end-to-end manner. The advantages of learning associative latent encodings are further highlighted with the examples of inferring upon incomplete input images. A comparison with alternative methods demonstrates the superiority of the present approach in these challenging tasks.

This paper describes the design and development of YOLO, a social robot aimed at boosting creativity in children. Creativity is one of the most sought-after competencies as we move from industrialized economies, in which standardized knowledge was imperative, to creative economies, where the ability to innovate is crucial for the workforce. YOLO is a robot to be used by children as a tool to boost new ideas and stimulate their creativity. This paper describes how established educational strategies that enhance creativity were combined with co-designing with children as informants to reach the the prototype design of the robot.

In this paper, we propose a taxonomy of handwriting errors exhibited by children as a way to build adequate strategies for integration with a co-writing peer. The exploration includes the collection of letters written by children in an initial study, which were then revised in a second study. The second study also analyses the "peer-learning" (PL) and "peer-tutoring" (PT) learning methods in an educational scenario, where a pair of children perform a collaborative writing activity in the presence of a robot facilitator. The data obtained in the first two studies allowed us to create a "taxonomy of handwriting errors". A set of writing errors were selected and implemented in an educational activity for validation. This activity constituted a third study, wherein we systematically induced the errors into a Nao robot's handwriting using the {PT} method - A teacher-child corrects the handwriting errors of the learner-robot. The preliminary results suggest that the children in general showed awareness to the writing errors and were able to perceive the writing abilities of the robot.

This paper presents ERIK, an inverse kinematics technique for robot animation that is able to control a real expressive manipulator-like robot in real-time, by simultaneously solving for both the robot's full body expressive posture, and orientation of the end-effector (head). Our solution, meant for generic autonomous social robots, was designed to work out of the box on any kinematic chain, and achieved by blending forward kinematics (for posture control) and inverse kinematics (for the gaze-tracking/orientation constraint). Results from a user study show that the resulting expressive motion is still able to convey an expressive intention to the users.

Automatic personality analysis has gained attention in the last years as a fundamental dimension in human-To-human and human-To-machine interaction. However, it still suffers from limited number and size of speech corpora for specific domains, such as the assessment of children's personality. This paper investigates a semi-supervised training approach to tackle this scenario. We devise an experimental setup with age and language mismatch and two training sets: A small labeled training set from the Interspeech 2012 Personality Sub-challenge, containing French adult speech labeled with personality OCEAN traits, and a large unlabeled training set of Portuguese children's speech. As test set, a corpus of Portuguese children's speech labeled with OCEAN traits is used. Based on this setting, we investigate a weak supervision approach that iteratively refines an initial model trained with the labeled data-set using the unlabeled data-set. We also investigate knowledge-based features, which leverage expert knowledge in acoustic-prosodic cues and thus need no extra data. Results show that, despite the large mismatch imposed by language and age differences, it is possible to attain improvements with these techniques, pointing both to the benefits of using a weak supervision and expert-based acoustic-prosodic features across age and language.

This paper presents a study on collaborative manipulation between an autonomous robot and multiple users. We investigate how different motion types impact people's ability to understand the robot's goals in a multi-user scenario. We propose an approach based on Collaborative Probabilistic Movement Primitives to generate the robot's movements, exploiting predictability and legibility of movement to express intentions through motion. We compare the impact on the interaction of using only either predictable or legible movements, and propose a third approach - hybrid motion - that selects, in each situation, whether to execute a predictable motion or a legible motion, depending on what the robot perceives as more efficient for the multi-user collaboration effort. To test the impact of the three motion types in the context of a collaborative task, we run a user study using a Baxter robot that autonomously serves cups of water to three users upon request. Our results show that, in the particular case where all users simultaneously request water, the hybrid motion performs better than the other two.

This article surveys the area of computational empathy, analysing different ways by which artificial agents can simulate and trigger empathy in their interactions with humans. Empathic agents can be seen as agents that have the capacity to place themselves into the position of a user’s or another agent’s emotional situation and respond appropriately. We also survey artificial agents that, by their design and behaviour, can lead users to respond emotionally as if they were experiencing the agent’s situation. In the course of this survey, we present the research conducted to date on empathic agents in light of the principles and mechanisms of empathy found in humans. We end by discussing some of the main challenges that this exciting area will be facing in the future.

The goal of designing autonomous and successful agents is often attempted by providing mechanisms to choose actions that maximise some reward function. When agents interact with a static environment, the provided reward functions are well-defined and the implementation of traditional learning algorithms turns to be feasible. However, agents are not only intended to act in isolation. Often, they interact in dynamic multiagent systems whose decentralised nature of decision making, huge number of opponents and evolving behaviour stems a complex adaptive system [3]. This way, it is an important challenge to unveil the long term outcome of agents’ strategies, both in terms of individual goals and social desirability [9]. This endeavour can be conveniently achieved through the employment of new tools from, e.g., population dynamics [4] and complex systems research, in order to grasp the effects of implementing agents whose strategies, even rational in the context of static environments, may turn to be disadvantageous (individually and socially) when successively applied by members of a dynamic population. In this paper, we present a paradigmatic scenario in which behavioural errors are pernicious if committed in isolation, yet are the source of long-term success when considering adaptive populations. Moreover, errors support population states in which fairness (less inequality) is augmented. We assume that the goals and strategies of agents are formalised through the famous Ultimatum Game (UG) [2].We focus on the changes regarding the frequency of agents adopting each strategy, over time. This process of social learning, essentially analogous to the evolution of animal traits in a population, enables us to use the tools of Evolutionary Game Theory (EGT), originally applied in the context of theoretical biology [4]. We describe analytically the behavioural outcome in a discretised strategy space of the UG, in the limit of small exploration rates (or the socalled mutations) [1]. This allows us to replicate the results of largescale simulations [7], yet avoiding the burden of computational resources.

Creativity is one of the most important and pervasive of all human abilities. However, it seems to decline during school age years, in a phenomenon entitled “creative crisis”. As developed societies are shifting from an industrialized economy to a creative economy, there is a need to support creative abilities through life. With this work, we aim to use social robots as boosters for creative-driven behaviors with children.

In this video we present a social robotic player that is able to play a traditional card game in a social manner. The interaction takes place in a rich environment in which two teams of two players each compete to win the card game. Therefore, the robotic game player has a partner, and an opponent team of two other players. During each game, the robot explores both competitiveness with the opponent team and cooperation with its partner, conciliating the performance of players and the social dynamics that emerge during the game-play.

In this video submission, we describe a scenario developed in the EMOTE project. The overall goal of the EMOTE project is to develop an empathic robot tutor for 11-13 year old school students in an educational setting. The pedagogical domain here is to assist students in learning and testing their map-reading skills typically learned as part of the geography curriculum in schools. We show this scenario with a NAO robot interacting with the students whilst performing map-reading tasks on a touch-screen device in this video.

The CoWriter activity involves a child in a rich and complex interaction where he has to teach handwriting to a robot. The robot must convince the child it needs his help and it actually learns from his lessons. To keep the child engaged, the robot must learn at the right rate, not too fast otherwise the kid will have no opportunity for improving his skills and not too slow otherwise he may loose trust in his ability to improve the robot' skills. We tested this approach in real pedagogic/therapeutic contexts with children in difficulty over repeated long sessions (40-60 min). Through 3 different case studies, we explored and refined experimental designs and algorithms in order for the robot to adapt to the troubles of each child and to promote their motivation and self-confidence. We report positive observations, suggesting commitment of children to help the robot, and their comprehension that they were good enough to be teachers, overcoming their initial low confidence with handwriting.

In this paper we propose a methodology for the creation of social interaction strategies for human-robot interaction based on restricted-perception Wizard-of-Oz studies (WoZ). This novel experimental technique involves restricting the wizard's perceptions over the environment and the behaviors it controls according to the robot's inherent perceptual and acting limitations. Within our methodology, the robot's design lifecycle is divided into three consecutive phases, namely data collection, where we perform interaction studies to extract expert knowledge and interaction data; strategy extraction, where a hybrid strategy controller for the robot is learned based on the gathered data; strategy refinement, where the controller is iteratively evaluated and adjusted. We developed a fully-autonomous robotic tutor based on the proposed approach in the context of a collaborative learning scenario. The results of the evaluation study show that, by performing restricted-perception WoZ studies, our robots are able to engage in very natural and socially-aware interactions.

Human beings naturally assign roles to one another while interacting. Role assignment is a way to organize interpersonal encounters and can result in uncertainty decrease when facing a novel interaction with someone we just met, or even to rediscover new roles within previous relationships. When people interact with synthetic characters - such as robots - it seems they also assign roles to these agents, just as they do with humans. Within the field of human-robot interaction (HRI), robots are being developed to fulfill specific roles. This enables researchers to design concrete behaviors that match the desired role that a robot will play in a given task. It would then be expected that if a robot is developed with such a specific role, users too would assign the same role to that robot. In this paper, we study how children assign roles to an educational robot whose role is established from the beginning of the interaction. Our results show that although the role that the robot played was explicitly presented to children, they end up perceiving and assigning different roles for that robot. Moreover, we conclude that role assignment in educational HRI is a dynamic process in which the perceptions of children regarding the robot change over time as a consequence of continuous interactions.

Research in education has long established how children mutually influence and support each other's learning trajectories, eventually leading to the development and widespread use of learning methods based on peer activities. In order to explore children's learning behavior in the presence of a robotic facilitator during a collaborative writing activity, we investigated how they assess their peers in two specific group learning situations: peer-tutoring and peer-learning. Our scenario comprises of a pair of children performing a collaborative activity involving the act of writing a word/letter on a tactile tablet. In the peer-tutoring condition, one child acts as the teacher and the other as the learner, while in the peer-learning condition, both children are learners without the attribution of any specific role. Our experiment includes 40 children in total (between 6 and 8 years old) over the two conditions, each time in the presence of a robot facilitator. Our results suggest that the peer-tutoring situation leads to significantly more corrective feedback being provided, as well as the children more disposed to self-disclosure to the robot.

Robots are currently being developed to enter our lives and interact with us in different tasks. For humans to be able to have a positive experience of interaction with such robots, they need to trust them to some degree. In this paper, we present the development and evaluation of a social robot that was created to play a card game with humans, playing the role of a partner and opponent. This type of activity is especially important, since our target group is elderly people - a population that often suffers from social isolation. Moreover, the card game scenario can lead to the development of interesting trust dynamics during the interaction, in which the human that partners with the robot needs to trust it in order to succeed and win the game. The design of the robot's behavior and game dynamics was inspired in previous user-centered design studies in which elderly people played the same game. Our evaluation results show that the levels of trust differ according to the previous knowledge that players have of their partners. Thus, humans seem to significantly increase their trust level towards a robot they already know, whilst maintaining the same level of trust in a human that they also previously knew. Henceforth, this paper shows that trust is a multifaceted construct that develops differently for humans and robots.

In recent years, robots have gradually become incorporated in our society and therefore play more relevant role in social environments. These robots vary in form, some being more anthropomorphic than others. This, creates a need to study their interaction with the world. In this paper we used Sphero and BB-8, two robots with a simple spherical body devoid of verbal and other complex communication methods, to investigate how they can communicate intention to people. A set of behaviors based on pet behaviors was designed and tested in a controlled experiment, where the robot's aim was to convince a participant to follow it. We concluded that the use of these behaviors allows a robot to effectively communicate intention as well as create a bond with the participant, who would treat it as an equal, thereby engaging it in social interactions such as playing with it or talking to it.

In this paper, we present an experiment in the context of a child-robot interaction where we study the influence of the child-robot spatial arrangement on the child's focus of attention and the perception of the robot's performance. In the “Co-Writer learning by teaching” activity, the child teaches a Nao robot how to handwrite. Usually only face-to-face spatial arrangements are tested in educational child robot interactions, but we explored two spatial conditions from Kendon's F-formation, the side-by-side and the face-to-face formations in a within subject experiment. We estimated the gaze behavior of the child and their consistency in grading the robot with regard to the robot's progress in writing. Even-though the demonstrations provided by children were not different between the two conditions (i.e. the robot's learning didn't differ), the results showed that in the side-by-side condition children tended to be more indulgent with the robot's mistakes and to give it better feedback. These results highlight the influence of experimental choices in child-robot interaction.

Is a robot that shares explicitly its emotions with users more believable and friendly? In a previous study addressing this question, results suggested that an emotion sharing feature in a robot may have negative effects in the perception of that robot. Here, we address the same question but also take into account the “competence” of a robot executing a task, to understand if some kind of interaction occurs.

The way gaze cues are used in social interactions is by no means irrelevant because they are fundamentally important for understanding social interactions. In this paper, we argue that social conflict is a form of relating and that gaze clues are critical to understanding the underlying cognitive processes in this phenomenon. To learn more about conflict, we created an experimental setting that reduces real life to a mixed-motive game. We analyse the gaze patterns of 22 10- to 12-year-old children in specific game moments that could have been conductive to conflict. Our aim is to understand how subtle forms of conflict unfold, by analysing micro-level behaviours and establishing a link to high-level psychological constructs. Their gazes show that children are being more competitive or cooperative at different stages of the game. Children tend to avoid confrontation by averting face-directed gazes when they are asking for larger profits, and they gaze longer to attempt to persuade the other child.

This work addresses the challenge of creating virtual agents that are able to portray culturally appropriate behavior when interacting with other agents or humans. Because culture influences how people perceive their social reality it is important to have agent models that explicitly consider social elements, such as existing relational factors. We addressed this necessity by integrating culture into a novel model for simulating human social behavior. With this model, we operationalized a particular dimension of culture—individualism versus collectivism—within the context of an interactive narrative scenario that is part of an agent-based tool for intercultural training. Using this scenario we conducted a cross-cultural study in which participants from a collectivistic country (Portugal) were compared with participants from an individualistic country (the Netherlands) in the way they perceived and interacted with agents whose behavior was either individualistic or collectivistic, according to the configuration of the proposed model. In the obtained results, Portuguese subjects rated the collectivistic agents more positively than the Dutch but both countries had a similarly positive opinion about the individualistic agents. This experiment sheds new light on how people from different countries differ when assessing the social appropriateness of virtual agents, while also raising new research questions on this matter.

In order to explore the impact of integrating a robot as a facilitator in a collaborative activity, we examined interpersonal distancing of children both with a human adult and a robot facilitator. Our scenario involves two children performing a collaborative learning activity, which included the writing of a word/letter on a tactile tablet. Based on the learning-by-teaching paradigm, one of the children acted as a teacher when the other acted as a learner. Our study involved 40 children between 6 and 8 years old, in two conditions (robot or human facilitator). The results suggest first that the child acting as a teacher feel more responsible when the facilitator is a robot, compared to a human; they show then that the interaction between a (teacher) child and a robot facilitator can be characterized as being a reciprocity-based interaction, whereas a human presence fosters a compensation-based interaction.

For more than a century, empathy has been a central topic in the study of human emotion. It plays a crucial role in our everyday social life, having implications for the survival of the species. In the case of agents that inhabit virtual worlds and interact socially among each other and with humans, empathy has also been considered to be an important mechanism to promote engaging and believable interactions. However, creating empathic agents, until recently, has been accomplished mostly through the implementation of specific empathic behaviors or by using domain-dependent empirical models. In this article, we propose a generic computational model of empathy that is grounded on recent psychological theories about empathy. The proposed model treats empathy as a process in which the intensity of the empathic response is modulated by a set of factors that involve the relationship between the agents of the empathic interaction, namely, the similarity and affective link, as well as some characteristics of the empathizer agent, such as mood and personality. This model was implemented into an affective agent architecture, which was then used in an evaluation that had 77 participants. The results indicate that our empathy model, when used to simulate a social scenario with a small group of agents, significantly changed the way that the users perceived and described the interactions between those agents.

Humans have a tendency to consider media devices as social beings. Social agents and artificial opponents can be examined as one instance of this effect. With today's technology it is already possible to create artificial agents that are perceived as socially present. In this paper, we start by identifying the factors that influence perceptions of social presence in human-agent interactions. By taking these factors into account and by following previously defined guidelines for building socially present artificial opponents, a case study was created in which a social robot plays the Risk board game against three human players. An experiment was performed to ascertain whether the agent created in this case study is perceived as socially present. The experiment suggested that by following the guidelines for creating socially present artificial board game opponents, the perceived social presence of users towards the artificial agent improves.

There has been some studies in applying robots to education and recent research on socially intelligent robots show robots as partners that collaborate with people. On the other hand, serious games and interaction technologies have also proved to be important pedagogical tools, enhancing collaboration and interest in the learning process. This paper relates to the collaborative scenario in EMOTE EU FP7 project and its main goal is to develop and present the dialogue dimensions for a robotic tutor in a collaborative learning scenario grounded in human studies. Overall, seven dialogue dimensions between the teacher and students interaction were identified from data collected over 10 sessions of a collaborative serious game. Preliminary results regarding the teachers perspective of the students interaction suggest that student collaboration led to learning during the game. Besides, students seem to have learned a number of concepts as they played the game. We also present the protocol that was followed for the purposes of future data collection in human-human and human-robot interaction in similar scenarios.

In this paper, we describe the results of an interview study conducted across several European countries on teachers' views on the use of empathic robotic tutors in the classroom. The main goals of the study were to elicit teachers' thoughts on the integration of the robotic tutors in the daily school practice, understanding the main roles that these robots could play and gather teachers' main concerns about this type of technology. Teachers' concerns were much related to the fairness of access to the technology, robustness of the robot in students' hands and disruption of other classroom activities. They saw a role for the tutor in acting as an engaging tool for all, preferably in groups, and gathering information about students' learning progress without taking over the teachers' responsibility for the actual assessment. The implications of these results are discussed in relation to teacher acceptance of ubiquitous technologies in general and robots in particular.

The articles in this special section focus on the use of emotion technology and applications in computer games.

Social perception abilities are among the most important skills necessary for robots to engage humans in natural forms of interaction. Affect-sensitive robots are more likely to be able to establish and maintain believable interactions over extended periods of time. Nevertheless, the integration of affect recognition frameworks in real-time human-robot interaction scenarios is still underexplored. In this article, we propose and evaluate a context-sensitive affect recognition framework for a robotic game companion for children. The robot can automatically detect affective states experienced by children in an interactive chess game scenario. The affect recognition framework is based on the automatic extraction of task features and social interaction-based features. Vision-based indicators of the children’s nonverbal behaviour are merged with contextual features related to the game and the interaction and given as input to support vector machines to create a context-sensitive multimodal system for affect recognition. The affect recognition framework is fully integrated in an architecture for adaptive human-robot interaction. Experimental evaluation showed that children’s affect can be successfully predicted using a combination of behavioural and contextual data related to the game and the interaction with the robot. It was found that contextual data alone can be used to successfully predict a subset of affective dimensions, such as interest toward the robot. Experiments also showed that engagement with the robot can be predicted using information about the user’s valence, interest and anticipatory behaviour. These results provide evidence that social engagement can be modelled as a state consisting of affect and attention components in the context of the interaction.

Emotional synthetic characters are able to evaluate (appraise) events as positive or negative with their emotional states being triggered by several factors. Currently, the vast majority of models for appraisal in synthetic characters consider factors related to the goals and preferences of the characters. We argue that appraisals that only take into consideration these "personal" factors are incomplete as other more social factors, such as the normative and the social context, including in-group and out-group relations, should be considered as well. Without them, moral emotions such as shame cannot be appraised, limiting the believability of the characters in certain situations. We present a model for the appraisal of characters' actions that evaluates whether actions by in-group and out-group members which conform, or not, to social norms generate different emotions depending on the social relations between the characters. The model was then implemented in an architecture for virtual agents and evaluated with humans. Results suggest that the emotions generated by our model are perceived by the participants, taking into account the social context and that participants experienced very similar emotions, both in type and intensity, to the emotions appraised and generated by the characters.

Measuring evocative emotions in affective interactions has become a critical step for effective engagements with computers. Electro dermal activity is believed to accurately isolate sympathetic responses, revealing paths to excitement, attention and arousal, and to differentiate emotional states. However, the inability to deal with varying amplitude and length of responses across individuals has led to its use as a simple intensity barometer. Thus, two questions remain unanswered. To which extent can electro dermal activity be used to recognize emotions? How do electro dermal responses vary between human-to-human and human-to-robot interactions? To answer these questions, we propose a new method to mine the signal that surpasses the referred limitations, and conduct an extensive experiment to study the responses to emotion-evocative stimuli across different settings. Observations reveal emerging electro dermal patterns for each emotion and attractive accuracy levels for emotion recognition that increases when there is a link to the psychological traits of the subjects.

Modern school environments are usually populated with children from diverse ethnic, cultural and social backgrounds, bringing in different social norms and skills, diverse behaviours and often contradicting cooperation strategies. As a result, conflicts are inevitable and should be resolved as quickly and painlessly as possible, making sure that school life and the learning process continue as intended. The Siren serious game aims to educate 10-14 year old students on conflict management and resolution, presenting them with user- and culture-adaptive mini game scenarios, based on popular game genres and taking into account their affective expressivity and in-game behaviour to adjust the intensity of the conflict to better suit their needs and competencies.

This paper explores the issues that arise in the context of the migration of a robotic pet between different embodiments and the associated design challenges. In the following, we describe the perceptions that a group of children have of a dinosaur character crossing the boundary between its robotic embodiment (the Pleo commercial pet), and its virtual counterpart on a mobile phone. We analyse the children's perceptions of, as well as emotional reactions to, the migration of this character, and show how seemingly subtle variations in the migration process can affect the children's perception on the character and its embodiments. Among other findings, gaps in the migration process, or perceived unresponsiveness, appeared to be accompanied by anxiety in the participating children. Based on our results, we point to yet unsolved design challenges for migration in interactions with embodied characters, and offer insights for migration implementation.

The idea of autonomous social robots capable of assisting us in our daily lives is becoming more real every day. However, there are still many open issues regarding the social capabilities that those robots should have in order to make daily interactions with humans more natural. For example, the role of affective interactions is still unclear. This paper presents an ethnographic study conducted in an elementary school where 40 children interacted with a social robot capable of recognising and responding empathically to some of the children's affective states. The findings suggest that the robot's empathic behaviour affected positively how children perceived the robot. However, the empathic behaviours should be selected carefully, under the risk of having the opposite effect. The target application scenario and the particular preferences of children seem to influence the degree of empathy that social robots should be endowed with.

This paper describes our approach on the development of the expression of emotions on a robot with constrained facial expressions. We adapted principles and practices of animation from Disney™ and other animators for robots, and applied them on the development of emotional expressions for the EMYS robot. Our work shows that applying animation principles to robots is beneficial for human understanding of the robots' emotions.

Confronting conflicts and coping with them is part of social life, since conflicts seem to arise in almost every context and developmental stage of human life. The personal and collective gains that follow conflict resolution have motivated scholars across many research fields to advocate the use of pro-social mechanisms for resolution. The Siren serious game aims to support teachers' role to educate young people on how to resolve conflicts, by employing affect-aware, user- and cultural adaptivity to provide interesting and relevant conflict scenarios and resolution approaches.

Conflict is an unavoidable feature of life, but the development of conflict resolution management skills can facilitate the parties involved in resolving their conflicts in a positive manner. The goal of our research is to develop a serious game in which children may experiment with conflict resolution strategies and learn how to work towards positive conflict outcomes. While serious games related to conflict exist at present, our work represents the first attempt to teach conflict resolution skills through a game in a manner informed by sociological and psychological theories of conflict and current best practice for conflict resolution. In this paper, we present a computational approach to conflict generation and resolution. We describe the five phases involved in our conflict modeling process: conflict situation creation, conflict detection, player modeling and conflict strategy prediction, conflict management, and conflict resolution, and discuss the three major elements of our player model: assertiveness, cooperativeness, and relationship. Finally, we overview a simple resource management game we have developed in which we have begun experimenting with our conflict model concepts.

While computer games can give users many other forms of immersive entertainment, the social richness that exists in face to face interactions is being lost. In this paper we start by presenting a brief review on artificial board game opponents. Given the current limitations in such opponents and with the final goal of building a Risk game artificial opponent, we describe an empirical study where utterances that human players vocalize in their games were categorized and analyzed.

The design of an affect recognition system for socially perceptive robots relies on representative data: human-robot interaction in naturalistic settings requires an affect recognition system to be trained and validated with contextualised affective expressions, that is, expressions that emerge in the same interaction scenario of the target application. In this paper we propose an initial computational model to automatically analyse human postures and body motion to detect engagement of children playing chess with an iCat robot that acts as a game companion. Our approach is based on vision-based automatic extraction of expressive postural features from videos capturing the behaviour of the children from a lateral view. An initial evaluation, conducted by training several recognition models with contextualised affective postural expressions, suggests that patterns of postural behaviour can be used to accurately predict the engagement of the children with the robot, thus making our approach suitable for integration into an affect recognition system for a game companion in a real world scenario.

This paper gives a brief overview of the challenges for multi-model perception and generation applied to robot companions located in human social environments. It reviews the current position in both perception and generation and the immediate technical challenges and goes on to consider the extra issues raised by embodiment and social context. Finally, it briefly discusses the impact of systems that must function continually over months rather than just for a few hours.

The Inter-ACT (INTEracting with Robots - Affect Context Task) corpus is an affective and contextually rich multimodal video corpus containing affective expressions of children playing chess with an iCat robot. It contains videos that capture the interaction from different perspectives and includes synchronised contextual information about the game and the behaviour displayed by the robot. The Inter-ACT corpus is mainly intended to be a comprehensive repository of naturalistic and contextualised, task-dependent data for the training and evaluation of an affect recognition system in an educational game scenario. The richness of contextual data that captures the whole human-robot interaction cycle, together with the fact that the corpus was collected in the same interaction scenario of the target application, make the Inter-ACT corpus unique in its genre.

This paper presents FearNot!, an innovative anti-bullying intervention for 8-12 year old children, illustrating the potential of Virtual Learning Environments in providing supportive secure experiential learning for challenging social situations. The innovative technological approach taken to FearNot! is briefly described, along with an overview of the evaluation approach used to assess FearNot! with over 800 children in the UK and Germany. The demo of FearNot! provides participants with the opportunity to experience leading edge technology adopted for a classroom intervention.

Affect sensitivity is of the utmost importance for a robot companion to be able to display socially intelligent behaviour, a key requirement for sustaining long-term interactions with humans. This paper explores a naturalistic scenario in which children play chess with the iCat, a robot companion. A person-independent, Bayesian approach to detect the user's engagement with the iCat robot is presented. Our framework models both causes and effects of engagement: features related to the user's non-verbal behaviour, the task and the companion's affective reactions are identified to predict the children's level of engagement. An experiment was carried out to train and validate our model. Results show that our approach based on multimodal integration of task and social interaction-based features outperforms those based solely on non-verbal behaviour or contextual information (94.79 % vs. 93.75 % and 78.13 %).

Given the recent advances in robot and synthetic character technology, many researchers are now focused on ways of establishing social relations between these agents and humans over long periods of time. Early studies have shown that the novelty effect of robots and agents quickly wears out and that people change their attitudes and preferences towards them over time. In this paper, we study the role of social presence in long-term human-robot interactions. We conducted a study where children played chess exercises with a social robot over a five week period. With this experiment, we identified possible key issues that should be considered when designing social robots for long-term interactions.

In this paper we describe a robotic game buddy whose emotional behaviour is influenced by the state of the game. Using the iCat robot and chess as the game scenario, an architecture for incorporating emotions as a result of a heuristic evaluation of the state of the game was developed. The game buddy was evaluated in two ways. First, we investigated the effects of the characterpsilas emotional behaviour on the userpsilas perception of the game state. And secondly we compared a robotic with a screen based version of the iCat in terms of their influence on userpsilas enjoyment. The results suggested that userpsilas perception of the game increases with the iCatpsilas emotional behaviour, and that the enjoyment is higher when interacting with the robotic version.

Abstract This work proposes a real‐time virtual human multimodal expression model. Five modalities explore the affordances of the body: deterministic, non‐deterministic, gesticulation, facial, and vocal expression. Deterministic expression is keyframe body animation. Non‐deterministic expression is robotics‐based procedural body animation. Vocal expression is voice synthesis, through Festival, and parameterization, through SABLE. Facial expression is lip‐synch and emotion expression through a parametric muscle‐based face model. Inspired by psycholinguistics, gesticulation expression is unconventional, idiosyncratic, and unconscious hand gestures animation described as sequences of Portuguese Sign Language hand shapes, positions and orientations. Inspired by the arts, one modality goes beyond the body to explore the affordances of the environment and express emotions through camera, lights, and music. To control multimodal expression, this work proposes a high‐level integrated synchronized markup language—expressive markup language. Finally, three studies, involving a total of 197 subjects, evaluated the model in storytelling contexts and produced promising results. Copyright © 2006 John Wiley & Sons, Ltd.

There has been an increasing interest in exploring how recognition of a user's affective state can be exploited in creating more effective human-computer interaction. It has been argued that IUIs may be able to improve interaction by including affective elements in their communication with the user (e.g. by showing empathy via adequate phrasing of feedback.) This workshop will address a variety of issues related to the development of what we will call the affective loop: detection/modeling of relevant user's states, selection of appropriate system responses (including responses that are designed to influence the user affective state but are not overtly affective), as well as synthesis of the appropriate affective expressions.

In this paper we describe a way of controlling the emotional states of a synthetic character in a game (FantasyA) through a tangible interface named SenToy. SenToy is a doll with sensors in the arms, legs and body, allowing the user to influence the emotions of her character in the game. The user performs gestures and movements with SenToy, which are picked up by the sensors and interpreted according to a scheme found through an initial Wizard of Oz study. Different gestures are used to express each of the following emotions: anger, fear, happiness, surprise, sadness and gloating. Depending upon the expressed emotion, the synthetic character in FantasyA will, in turn, perform different actions. The evaluation of SenToy acting as the interface to the computer game FantasyA has shown that users were able to express most of the desired emotions to influence the synthetic characters, and that overall, players, especially children, really liked the doll as an interface.

Game development is an emerging area of development for new types of interaction between computers and humans. New forms of communication are now being explored there, influenced not only by face to face communication but also by recent developments in multi-modal communication and tangible interfaces. This demo will feature a computer game, FantasyA, where users can play the game by interacting with a tangible interface, SenToy (see Figure 1). The main idea is to involve objects and artifacts from real life into ways to interact with systems, and in particular with games. So, SenToy is an interface for users to project some of their emotional gestures through moving the doll in certain ways. This device would establish a link between the users (holding the physical device) and a controlled avatar (embodied by that physical device) of the computer game, FantasyA.

One difficulty in creating synthetic characters for interactive stories is that these characters must convey their role in the story in a believable way. However, the relation between believability, on one side, and the role a character plays in a drama, on the other, has not yet been fully addressed. In this paper we will present a view on how to develop believable synthetic characters whose behaviour is based on a set of predefined functions (Propp's functions) associated with the role they play in the story. To illustrate the approach, we will present a collaborative virtual environment, Teatrix, designed for children to build their own stories-fairy tales. In Teatrix, virtual actors play roles (such as villain, hero, magician, etc), which are functional for the development of the story. Such roles have pre-defined goals and plans, allowing the story to flow and climax situations to arise. Teatrixis already in use by children ages between 7 and 9, in the context of a Computer-Integrated Classroom scenario.

This paper presents a research approach for guidance and control of story characters by young children. We show that it is possible for a child to be at the same time an actor, performing a role in the story, and an engaged and empathic spectator of the overall story. To illustrate the approach, we describe Teatrix: a collaborative virtual environment for story creation by young children. In Teatrix, children control the actions of their story characters, which are implemented as intelligent agents in order to perform a certain chosen role, i.e., a villain or an hero. During the acting, children control their characters at different levels: at motor level, at behavioral level, at emotional level, and at reflection-level. In order to combine these different types of control, we introduce the concept of time-freeze in the story creation, combined with a tool for reflection, called "hot seating". In this way, children are able to control their characters more deeply while, at the same time, feel like part of the audience of a play.