With the rapid advancement of robotics, robots’ helping behaviors are increasingly framed not only as functional assistance but also as prosocially meaningful interaction. In this context, the resource cost borne by the help-provider is a critical factor, yet it has not been systematically explored in existing human-robot interaction (HRI) research. Understanding how humans perceive and respond to different types of helping is essential for building better human–robot relationships. This study addresses this gap through two experiments. Study 1 examined the role of agent resource type (robot’s own resources vs. external resources). Results showed that when robots shared their own resources, participants did not report significant differences in overall attitudes or prosocial behavior, but attributed higher performance trust and expressed stronger feelings of gratitude and guilt. Study 2 further examined the importance of agent resources (robot battery level: high vs. low) . The results showed that even when relative costs were the same, participants tended to perceive sharing from a low-battery robot as more reliable, while variations in resource type or importance did not significantly change social responses. These findings suggest that human evaluations of robots are shaped not only by the outcomes of helping but also by the perceived cost and sacrifice underlying robot actions. Our work offers an initial direction for integrating resource cost considerations into the design of social robots.

Until recently the game industry heavily relied on manual playtesting to test the games it produces. Even if the benefits of introducing automated testing are acknowledged, it is rarely done in practice. Some of the main hurdles include the lack of automated testing tools that can target computer games as well as the complexity of automated game plays which are much more difficult to program than typical simple test sequences. This article presents an agent-based testing framework called aplib that comes with a Domain Specific Language (DSL) that allows complex playtests to be programmed more abstractly. A so-called goal structure is used to abstractly formulate a playtest scenario in terms of main goals and their decomposition into subgoals. Scenarios that are not too complicated can be formulated using static goal structures. More complex scenarios may need a test agent that can dynamically adapt its play according to the situation that evolves during the play. To handle such cases, aplib allows dynamic goals to be expressed as well. Invariants and pre-/post-conditions are used to assert the properties that a play is expected to satisfy. They include differential properties that allow constraints on the current state to be related to that of past states. Three case studies are included in the article. The first one aims to evaluate the performance of playtests programmed with aplib . The second shows that the approach can also be combined with other automated testing approaches, in this case reinforcement learning. The third shows the applicability of such playtests in a 3D setup and for non-functional testing.

Access to public spaces is of the utmost importance for social cohesion, inclusion, and civic engagement.Nevertheless, a large majority of public spaces remain incredibly uncomfortable environments for neurodivergent individuals due to, for instance, the unpredictability of such spaces and the sensory stimuli within them.Smart City technologies present an exciting opportunity to improve the accessibility and enjoyment of the spaces where they are deployed by, for instance, offering users the ability to customise a space to their specific sensory needs.However, the research topic of public space technologies for neurodivergent individuals remains scattered and sparsely documented.This critical review analyses the existing domains of inquiry, contributing a theoretical framework based on Spatial Justice and Neuroqueer Technoscience and suggests future research avenues informed by this framework.We advocate for the participatory co-creation of a neurodivergent-affirming landscape of public space technologies that both support neurodivergent needs and promote neurodivergent joy.

In this article, we conduct a systematic review on procedural content generation (PCG) for cooperative games. Our main goal was exploring the state of the art in this area of research, and, in particular, its application to level generation for cooperative games. Another objective was determining how generating content for cooperative games could be different from generating content for other types of games. Our results showed a lack of research in this particular application of PCG. Despite this, we found a variety of use cases of PCG for cooperative games, most of them related toautonomous generationandco-creative and mixed-initiative design. In addition, we found a variety of methods and techniques being used to generate levels for cooperative games that covered the broad categories we used to condense this information. Concerning the differences between generating content for cooperative games and generating content for other types of games, the studies we found suggested that PCG for cooperative games should take into account the design specificities of cooperative games—the types of tasks, cooperative game design patterns and game mechanics used to incentivize and support cooperative gameplay.

State-of-the-art agent-modelling tools support the creation of powerful Socially Intelligent Agents (SIAs) capable of engaging in social interactions with participants in various roles and environments. However, their deployment demands a labourious authoring task as it is necessary to manually define behaviour rules and create content for different interaction scenarios. While Socially Intelligent Agents (SIAs) research has centred on the user experience, we shift focus to the authors. To understand the challenges faced by authors who create these agents, we performed an innovative analysis of the authoring experience in modern agent modelling tools. One key finding is that, while SIA concepts are generally understandable, emotional-based concepts are not as easily comprehended or used by authors. We propose a hybrid solution approach that culminated in the development of Authoring-Assisted FAtiMA-Toolkit. The augmented agent modelling tool incorporates a data-driven Authoring Assistant to boost author productivity while promoting transparency and authorial control. To evaluate the impact of this framework on the authoring experience, we conducted a user study. Results showed that authors using the Authoring-Assisted FAtiMA-Toolkit were on average able to create more SIA-related content in less time. Our findings suggest that data-augmented, theory-grounded agent modelling tools can support the development of affective social agents by reducing the authoring burden without sacrificing the framework’s clarity or the authors’ control over the content.

Dynamics in Human-AI interaction should lead to more satisfying and engaging collaboration. Key open questions are how to design such interactions and the role personal goals and expectations play. We developed three AI partners of varying initiative (leader, follower, shifting) in a collaborative game called Geometry Friends. We conducted a within-subjects experiment with 60 participants to assess personal AI partner preference and performance satisfaction as well as perceived warmth and competence of AI partners. Results show that AI partners following human initiative are perceived as warmer and more collaborative. However, some participants preferred AI leaders for their independence and speed, despite being seen as less friendly. This suggests that assigning a leadership role to the AI partner may be suitable for time-sensitive scenarios. We identify design factors for developing collaborative AI agents with varying levels of initiative to create more effective human-AI teams that consider context and individual preference.

Neurodivergent children spend most of their time in neurodiverse schools alongside their neurotypical peers and often face social exclusion. Inclusive play activities are a strong vehicle of inclusion. Unfortunately, games designed for the specific needs of neurodiverse groups are scarce. Given the potential of robots to support play, we led a co-design process to build an inclusive robotic game for neurodiverse classrooms. We conducted five co-design workshops, engaging 80 children from neurodiverse classrooms in designing an inclusive game. Employing the resulting design insights, we iteratively prototyped and playtested a tabletop robotic game leveraging off-the-shelf robots. Reflecting upon our findings, we discuss how the longitudinal co-design process (rather than the resulting game) was key in allowing children the space to learn how to accommodate accessibility needs and create inclusive play experiences. We posit the use of co-design to enhance children’s interpersonal relationships, fosters feelings of ownership, and encourages appropriation practices as a strategy to sustain inclusive experiences that extend beyond project timelines or artefact designs.

Play is a central aspect of childhood development, with games as a vital tool to promote it. However, neurodivergent children, especially those in neurodiverse environments, are underserved by HCI games research. Most existing work takes on a top-down approach, disregarding neurodivergent interest for the majority of the design process. Co-design is often proposed as a tool to create truly accessible and inclusive gaming experiences. Nevertheless, co-designing with neurodivergent children within neurodiverse groups brings about unique challenges, such as different communication styles, sensory needs and preferences. Building upon recommendations from prior work in neurodivergent, mixed-ability, and child-led co-design, we propose a concrete participatory game design kit for neurodiverse classrooms: PartiPlay. Moreover, we present preliminary findings from an in-the-wild experiment with the said kit, showcasing its ability to create an inclusive co-design process for neurodiverse groups of children. We aim to provide actionable steps for future participatory design research with neurodiverse children.

Friendships are a fundamental source of support during challenging times, especially among pre-adolescents. The current pandemic situation makes it even harder to rely on support from their peers or strengthen friendships. To accompany and support pre-adolescents outside of school at a moment where most interactions happen online, we propose the HUB, a novel online interactive social space. The HUB is a safeguarded and monitored social space which seeks to improve social well-being and positive reinforcement practices between peers by design. This paper’s key contributions derived from designing the HUB are threefold: an online social space which follows an iterative user-centered design approach; it is grounded on a theoretical model of friendship development to scaffold interactions of dyadic relationships that occur on the HUB; and it employs a set of gamification strategies, such as quests, achievements and rewards to keep pre-adolescents motivated, and, particularly, an acknowledgement system that encourages peers to work on, and acknowledge, character strengths and social skills in others, which are fundamental for their development as individuals.

To be considered socially intelligent, agents must be able to adjust their behavior to different social contexts. However, those adjustments must be recognized and understood by other agents and humans, particularly to identify the social motivations that triggered such changes. In this work, we report on an experimental study that explores how external observers attribute social motivations to behavior and appearance changes that agents make to adapt to the context. Furthermore, we study how the presence of other agents in the context might impact the attribution of motivations to specific changes. Our results indicate that participants identify agents’ appearance changes as more socially and spatially motivated than behavioral changes. Additionally, agents’ awareness is rated higher when the agents adjust their physical characteristics compared to behavioral changes.

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.

The authoring of interactive dialogues in video games is an overwhelming and complex task for game writers. Developing an Interactive Narrative that balances authorial intent and players' agency requires frequent in-depth testing. The limited range of tools to assist authors in verifying their story can limit the creation of more complex narratives. In this paper, we discuss the challenges of Interactive Story design and provide a model consisting of a set of metrics for testing interactive dialogues. Using this model, we developed a prototype for the Story Validator tool. This debugging tool allows game writers to experiment with different hypotheses and narrative properties in order to identify inconsistencies in the authored narrative and predict the output of different playthroughs with visual representation support. We conducted a series of user tests, Using the Story Validator, to investigate whether the tool adequately helps users identify problems that appear in the game's story. The results showed that the tool enables content creators to easily test their stories, setting our model as a good step towards automated verification for assistance of authoring interactive narratives.

Social agents should exhibit socially adequate behavior to fit the context they meet. Fitting the context is particular relevant for interactive agents that interact and are being observed by people. Hence, the perceptions of people of such social capabilities are an important concern. Exhibiting socially adequate behavior can more easily be identifiable when in the presence of other social actors. However, even alone, one’s ability to adjust to the context might be socially motivated and interpreted as such. Similarly, intelligent agents may be identified as social beings when acting alone. Moreover, social context is triggered in different ways. In this study, we explore if adaptation to the physical surroundings (e.g., the agent’s location) is enough to shape the perceptions of people observing the agent. We contribute to the study of situated cognition’s role in interpreting an autonomous agent’s behavior. In particular, we explore the impact of behavior changes grounded on the location as a contextual cue on the motivation ascribed by an observer to the agent’s behavior.

Social sharing of emotions (SSE) occurs when one communicates their feelings and reactions to a certain event in the course of a social interaction. The phenomenon is part of our social fabric and plays an important role in creating empathetic responses and establishing rapport. Intelligent social agents capable of SSE will have a mechanism to create and build long-term interaction with humans. In this paper, we present the Emotional Episode Generation (EEG) model, a fine-tuned GPT-2 model capable of generating emotional social talk regarding multiple event tuples in a human-like manner. Human evaluation results show that the model successfully translates one or more event-tuples into emotional episodes, reaching quality levels close to human performance. Furthermore, the model clearly expresses one emotion in each episode as well as humans. To train this model we used a public dataset and built upon it using event extraction techniques1.

This paper explores robotic leadership and the impact of different human-leadership styles in teams leaded by social robots. It is anticipated that social robots can have the capability to serve as leaders for human teams and to collaborate with humans in order to improve the organizational requirements of the workplace environment. We report an experiment conducted in order to determine which human-leadership styles would be associated with better results in organizational phenomena, such as, employee productivity, role ambiguity, engagement and employee satisfaction. The sample comprised 108 collaborators divided in 36 teams, who had to perform a collaborative task with a robotic leader acting according to two different leadership styles. The experiment results showed that both leadership styles can have positive impacts in organizational outcomes, although in different aspects. These findings yield important insights for the creation of robotic partners and for the successful introduction of robots as leaders of human teams.

Game artificial intelligence (AI) competitions are important to foster research and development on Game AI and AI in general. These competitions supply different challenging problems that can be translated into other contexts, virtual or real. They provide frameworks and tools to facilitate the research on their core topics and provide means for comparing and sharing results. A competition is also a way to motivate new researchers to study these challenges. In this article, we present theGeometry Friendsgame AI competition.Geometry Friendsis a two-player cooperative physics-based puzzle platformer computer game. The concept of the game is simple, though its solving has proven to be difficult. While the main and apparent focus of the game is cooperation, it also relies on other AI-related problems such as planning, plan execution, and motion control, all connected to situational awareness. All of these must be solved in real-time. In this article, we discuss the competition and the challenges it brings, and present an overview of the current solutions.

This paper discusses how quizzes are applied within the field of software engineering learning and how quizzes can help self-regulate student learning. For this, a systematic mapping that selected the most relevant studies on the use of quizzes in education was performed, aiming to clarify their relationships and mutual impacts. Our analysis shows that student engagement and quiz work is a prominent learning solution for increasing motivation in and out of the classroom. We found that quizzes can be applied in software engineering and through generic quizzes, online Quizzes, pop-Quizzes, gamified Quizzes, in quiz games or, alternatively, as an exercise in creating quizzes. However, we did not find approaches to the use of quizzes, explicitly containing Zimmerman's cyclic model, only some of the model's activities in isolation and not explicitly. We argue that sharing the quizzes will raise the potential for then to be use as a self-regulation tool in software engineering education. We describe the steps taken by a Software Engineering Gamification project to create a effective tool for creating and sharing software engineering quizzes. Our next requirement to be implemented in the project will be the application of self-regulation of learning containing the three phases of the Zimermman cyclic model.

Few if any would dispute that educating software engineering is a challenging endeavour. Although programming and creating new artefacts can motivate the creativity of students. Other software engineering topics (like e.g. requirement specifications and testing) are not considered very exciting by students. However, these topics are important to develop quality software and insufficient knowledge of students - Europe's future software engineers - in the long run contributes to failing software. The EU Erasmus+ project IMPRESS was set to explore the use of gamification in educating software engineering at the university level. The objective has been to develop a toolset that can help to improve students' engagement, and hence their appreciation, for the taught subjects like software testing and specifications. The proposed tutorial will guide participants through the set of tools developed by the project and introduce how they can use them to improve students' engagement.

Testing for quality assurance (QA) is a crucial step in the development of Extended Reality (XR) systems that typically follow iterative design and development cycles. Bringing automation to these testing procedures will increase the productivity of XR developers. However, given the complexity of the XR environments and the User Experience (UX) demands, achieving this is highly challenging. We propose to address this issue through the creation of autonomous cognitive test agents that will have the ability to cope with the complexity of the interaction space by intelligently explore the most prominent interactions given a test goal and support the assessment of affective properties of the UX by playing the role of users.

In this work we test the hypothesis that interacting with an intelligent virtual character in Virtual Reality (VR) has a stronger impact compared to the same interaction in a traditional non-immersive platform, both in terms of presence and believability.

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.

In this paper, we propose a feature-based model to recognize emotions via touching patterns of individuals playing a game on a typical tablet. In this work, novel features, such as Angular Velocity/Acceleration, Angle, Curl, Area and number of strokes within a time window, are introduced and the gold-standard of the data is determined automatically via subjects' facial expressions. The results show that the approach is promising and the model is able to recognize all the six basic emotions, with a performance of 71.92 % ±0.51. In addition, the recognition of valence and arousal reaches correlation coefficients equal to 0.76 and 0.78 respectively.

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.

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.

This research full paper describes our work on the development, and evaluation of a physically-embodied robot teacher (PERT) as a facilitator for Peer Instruction (PI) in a high school science classroom. Embodied pedagogical agents are particularly useful in teaching young learners because of the engagement, and enjoyment they engender. Teaching robots are among possibilities in the future classroom; they may also represent solutions to the looming, acute global teacher scarcity. However, their design creates unique computational challenges. Moreover, robot-human systems involving a single robot in interaction with multiple humans, are not common. PI has been reported in many studies as a learner-centered approach that supports conceptual learning, metacognition, cognitive load sharing, and higher order learning, through peer scaffolding. However, existing studies have reported only applications by human teachers in regular classrooms. This study highlights an approach for enabling a PERT as a PI facilitator. Based on conflicting reports of the significance of agent embodiment for learning, we present the report of two experimental studies from our assessment of the system, employing the mixed method approach. We compared participants' perception of the robot's social presence and teaching quality when the robot is physically present and when it is absent (voice-only). Our findings are based on data from 14 participants in each study. The Social Presence indeX (SPiX) and the teacher quality index (TQI) were used for quantitative data collection for social presence and teaching quality, respectively. Qualitative data were based on live observation and participants' open-ended feedback. Quantitative findings show no significant difference in participants' perceptions in both conditions but qualitative feedback provided more information. Our findings show: i) students are willing to accept non-human teachers, and ii) their expectations of them are similar to those for human teachers; iii) an improved affective system will promote increased presence; iv) participants noted the robot as `neutral', with important implications for culturally-responsive education; v) robot teachers could be potential technology-inspired solution to global teacher scarcity and vi) in addition to promoting deeper learning, PI can be a useful pedagogical approach for non-human teachers. This study is in line with anticipated ability of future technological advancements to support machines that are as effective as intelligent human tutors.

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.

Social Power, a pervasive feature in our daily life, has been proved to have a significant impact on Social Interaction; While the capability of maintaining a Social Interaction has an acknowledged role in Believability of Intelligent Virtual Agents (IVAs). In this paper, we argue that an ability of reasoning and planning in the presence of Social Power enhances Social Believability of IVAs, leading to more rational interactions. With this aim, we focus on theoretical issues of agent modeling aiming at increasing intelligence and therefore believability of NPCs or agents of game-like simulations or serious games. Thereby, we propose a model of social power inspired by a recently proposed model, SAPIENT, based on a well-known theory of Social Power proposed by French and Raven.

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.

Social power is a pervasive feature with acknowledged impact in a multitude of social processes. However, despite its importance, common approaches to social power interactions in multi-agent systems are rather simplistic and lack a full comprehensive view of the processes involved. In this work, we integrated a comprehensive model of social power dynamics into a cognitive agent architecture based on an operationalization of different bases of social power inspired by theoretical background research in social psychology. The model was implemented in an agent framework that was subsequently used to generate the behavior of virtual characters in an interactive virtual environment. We performed a user study to assess users' perceptions of the agents and found evidence supporting both the social power capabilities provided by the model and their value for the creation of believable and interesting scenarios. We expect that these advances and the collected evidence can be used to support the development of agent systems with an enriched capacity for social agent simulation.

An important requirement of household energy simulation models is their accuracy in estimating energy demand and its fluctuations. Occupant behavior has a major impact upon energy demand. However, Markov chains, the traditional approach to model occupant behavior, (1) has limitations in accurately capturing the coordinated behavior of occupants and (2) is prone to over-fitting. To address these issues, we propose a novel approach that relies on a combination of data mining techniques. The core idea of our model is to determine the behavior of occupants based on nearest neighbor comparison over a database of sample data. Importantly, the model takes into account features related to the coordination of occupants' activities. We use a customized distance function suited for mixed categorical and numerical data. Further, association rule learning allows us to capture the coordination between occupants. Using real data from four households in Japan we are able to show that our model outperforms the traditional Markov chain model with respect to occupant coordination and generalization of behavior patterns.

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.

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.

If autonomous agents interact with people, they should achieve the suspension of disbelief in order to offer a good interaction experience to users. To achieve this it is important that the agents' behaviours are consistent with a given personality since people have a tendency to attribute personality to interactive artifacts. The concept of personality is also useful to create diversity in multi-agent simulations even if users do not directly engage in the interactions, for example, to explore different strategies in simulated societies. This paper presents a computational model of personality based on the Five Factor Model of personality for the behaviour of social autonomous agents that interact in teamwork scenarios.

Agriculture has a strong impact in the environment; it has played key role in the development of food security and food safety. In this paper, we are proposing a 3D multilingual Internet game, developed by using the on-line virtual world platform, that lets the player experience the potential effects of agriculture in the environment. The main idea is to foster awareness of agriculture's environmental issues, by not only making the player learn the impact of fertilizers and deforestation that affect the sources of water and weather, respectively, but also enhance the importance of food quality. To make players care for these issues, the game creates a direct impact by using the happiness of people that is represent by the villagers. To reduce the language barrier when sharing knowledge across the countries is needed, the game also supports multi-language to make it more understandable to the player. This paper describes details of the game design, the system architecture and the experiment. The experiment conducted with the game showed promising results.

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.