Interactive Play Spaces can support positive behaviour. Play endings, user experience (UX), and replay intention, can play an important role to achieve this. However, the relation between these aspects is underexplored. We explore how different types of endings–open, closed positive (winning), and closed negative (losing)–affect user experience and attitude towards replay in a semi-situated study with 93 adults in a science center. While assigned ending conditions did not significantly influence reported experience, many participants, significantly in the open-ended condition, perceived their assigned ending condition differently. Analysis on these self-reported endings revealed that players who experienced a closed negative ending reported higher Stimulation (UEQ). Additionally, user experience dimensions (Attractiveness, Dependability, Stimulation) and Positive Affect (I-PANAS-SF) positively related to attitude towards replay. These findings provide insights into the relation between play endings, UX and attitude towards replay, and highlight the importance of inquiring about experienced experimental conditions in user research.

Computational models can advance affective science by shedding light onto the interplay between cognition and emotion from an information processing point of view. We propose a computational model of emotion that integrates reinforcement learning (RL) and appraisal theory, establishing a formal relationship between reward processing, goal-directed task learning, cognitive appraisal, and emotional experiences. The model achieves this by formalizing four evaluative checks from the component process model (CPM) in terms of temporal difference learning updates: suddenness, goal relevance, goal conduciveness, and power. The formalism is task independent and can be applied to any task that is represented as a Markov decision problem (MDP) and solved using RL. We evaluate the model by predicting a range of human emotions based on a series of vignette studies, highlighting its potential to improve our understanding of the role of reward processing in affective experiences.

Predicting users’ emotional states during interaction is a long-standing goal of affective computing. However, traditional methods based on sensory data alone fall short due to the interplay between users’ latent cognitive states and emotional responses. To address this, we introduce a computational cognitive model that simulates emotion as a continuous process, rather than a static state, during interactive episodes. This model integrates cognitive-emotional appraisal mechanisms with computational rationality, utilizing value predictions from reinforcement learning. Experiments with human participants demonstrate the model’s ability to predict and explain the emergence of emotions such as happiness, boredom, and irritation during interactions. Our approach opens the possibility of designing interactive systems that adapt to users’ emotional states, thereby improving user experience and engagement. This work also deepens our understanding of the potential of modeling the relationship between reward processing, reinforcement learning, goal-directed behavior, and appraisal.

Whereas the reception task is a promising application domain for social robots, knowledge is lacking about how to design the appropriate re-usable communication styles for a reception robot. This paper presents the use and evaluation of an iterative interaction-design (ID) method with which task- and person-oriented multi-modal communication styles have been designed for such a robot. First, we report on an evaluation study of the ID-method with Industrial Design students (N =13) who designed these two communication styles for a Pepper robot. This provided a set of distinct designs of the two styles, for which the differences in design parameters were in line with social science theory. The task-oriented style showed a more formal, shorter and less chatty communication. Second, we present findings from a Mechanical Turk study conducted to evaluate the perception of these style designs. Participants (N =301) were presented with videos showing the robot acting as a receptionist and were asked to rate their perception of the robot, the service experience and the orientation of the designs. Overall, the interaction with the robot was appreciated well. The robot with a person-oriented style was perceived to be more animate and likeable. Analysis showed that chit-chat was the main contributor to the perceived difference between the person-oriented and task-oriented styles. This is an important finding as it gives interaction designers a validated best-practice approach to make interaction style more or less personal.

The Participant Journey Map (PJM) provides structured insight into participation with interactive play in (semi-) public environments. It supports understanding of participants’ behavior and was developed based on experiences with previously developed playful interfaces, related research and expert interviews. We apply the PJM to interactive playful museum exhibits and evaluate and refine it based on its usage in a situated context. We observed 672 play sessions with 6 interactive playful museum exhibits. The observation data was visualized and analyzed using the PJM. This study shows that the PJM provides a realistic representation of participant behaviour, can be used to identify stagnations and progressions in participation flow, and support identification of influencing design and contextual factors. With this paper we contribute by presenting the PJM as a well-grounded, valuable and realistic framework for evaluating and understanding participation with situated interactive play, based on post-hoc evaluation of multiple interfaces with many users.

Large language models, in particular generative pre-trained transformers (GPTs), show impressive results on a wide variety of language-related tasks. In this paper, we explore ChatGPT’s zero-shot ability to perform affective computing tasks using prompting alone. We show that ChatGPT a) performs meaningful sentiment analysis in the Valence, Arousal and Dominance dimensions, b) has meaningful emotion representations in terms of emotion categories and these affective dimensions, and c) can perform basic appraisal-based emotion elicitation of situations based on a prompt-based computational implementation of the OCC appraisal model. These findings are highly relevant: First, they show that the ability to solve complex affect processing tasks emerges from language-based token prediction trained on extensive data sets. Second, they show the potential of large language models for simulating, processing and analyzing human emotions, which has important implications for various applications such as sentiment analysis, socially interactive agents, and social robotics.

In this article we introduceMementos: the first multimodal corpus for computational modeling of affect and memory processing in response to video content. It was collected online via crowdsourcing and captures 1995 individual responses collected from 297 unique viewers responding to 42 different segments of music videos. Apart from webcam recordings of their upper-body behavior (totaling 2012 minutes) and self-reports of their emotional experience, it contains detailed descriptions of the occurrence and content of 989 personal memories triggered by the video content. Finally, the dataset includes self-report measures related to individual differences in participants’ background and situation (Demographics,Personality, andMood), thereby facilitating the exploration of important contextual factors in research using the dataset. We describe 1) the construction and contents of the corpus itself, 2) analyse thevalidityof its content by investigating biases and consistency with existing research on affect and memory processing, 3) review previously published work that demonstrates theusefulnessof the multimodal data in the corpus for research on automated detection and prediction tasks, and 4) provide suggestions for how the dataset can be used in future research on modelingVideo-Induced Emotions,Memory-Associated Affect, andMemory Evocation.

Making the transition to long-term interaction with social-robot systems has been identified as one of the main challenges in human-robot interaction. This article identifies four design principles to address this challenge and applies them in a real-world implementation: cloud-based robot control, a modular design, one common knowledge base for all applications, and hybrid artificial intelligence for decision making and reasoning. The control architecture for this robot includes a common Knowledge-base (ontologies), Data-base, “Hybrid Artificial Brain” (dialogue manager, action selection and explainable AI), Activities Centre (Timeline, Quiz, Break and Sort, Memory, Tip of the Day, \( \ldots \) ), Embodied Conversational Agent (ECA, i.e., robot and avatar), and Dashboards (for authoring and monitoring the interaction). Further, the ECA is integrated with an expandable set of (mobile) health applications. The resulting system is a Personal Assistant for a healthy Lifestyle (PAL), which supports diabetic children with self-management and educates them on health-related issues (48 children, aged 6–14, recruited via hospitals in the Netherlands and in Italy). It is capable of autonomous interaction “in the wild” for prolonged periods of time without the need for a “Wizard-of-Oz” (up until 6 months online). PAL is an exemplary system that provides personalised, stable and diverse, long-term human-robot interaction.

Robots and virtual agents need to adapt existing and learn novel behavior to function autonomously in our society. Robot learning is often in interaction with or in the vicinity of humans. As a result the learning process needs to be transparent to humans. Reinforcement Learning (RL) has been used successfully for robot task learning. However, this learning process is often not transparent to the users. This results in a lack of understanding of what the robot is trying to do and why. The lack of transparency will directly impact robot learning. The expression of emotion is used by humans and other animals to signal information about the internal state of the individual in a language-independent, and even species-independent way, also during learning and exploration. In this article we argue that simulation and subsequent expression of emotion should be used to make the learning process of robots more transparent. We propose that the TDRL Theory of Emotion gives sufficient structure on how to develop such an emotionally expressive learning robot. Finally, we argue that next to such a generic model of RL-based emotion simulation we need personalized emotion interpretation for robots to better cope with individual expressive differences of users.

Empirical evidence suggests that the emotional meaning of facial behavior in isolation is often ambiguous in real-world conditions. While humans complement interpretations of others' faces with additional reasoning about context, automated approaches rarely display such context-sensitivity. Empirical findings indicate that the personal memories triggered by videos are crucial for predicting viewers' emotional response to such videos ?- in some cases, even more so than the video's audiovisual content. In this article, we explore the benefits of personal memories as context for facial behavior analysis. We conduct a series of multimodal machine learning experiments combining the automatic analysis of video-viewers' faces with that of two types of context information for affective predictions: \beginenumerate* [label=(\arabic*)] \item self-reported free-text descriptions of triggered memories and \item a video's audiovisual content \endenumerate*. Our results demonstrate that both sources of context provide models with information about variation in viewers' affective responses that complement facial analysis and each other.

Virtual agents are increasingly being used for communication training such as public speaking-, job interviews-, as well as negotiation training. In these use-cases the agent is generally taking on the role of interviewer and its behaviour is altered according to the nonverbal cues of its human interlocutor. However, understanding how the agent's non-verbal cues influence human behaviour, perception or interactions outcomes is equally important. This contributes to appropriate behaviour generation in agents, but also to our understanding of the intricate interplay of non-verbal behaviours on human perception and interaction outcomes.

The design space of human-robot interaction is large and multi-dimensional. A sound design requires a systematic theory-driven exploration, specification and refinement of design variables. There is a need for a practical method and tool to iteratively specify the content of the dialogue (e.g., speech acts) with the accompanying expressive behavior (e.g., gesture openness) as prescribed by social science theory, e.g., task- and person-oriented communication. This paper presents an iterative interaction-design (ID) method for multi-modal robot communication. Following the ID-method, a designer first creates his/her "own" individual design and, subsequently, provides an iteration to the evolving iterative design. To support the design method, we developed an ID-tool (available for download). The tool support entails (a) selecting the theory-based communication style; (b) creating and linking the dialogue act components for the concerning use case; and (c) setting the associated expression parameters.We conducted a study with Industrial Design students (N = 13) who followed the ID-method and used our tool to design person- and task-oriented communications for a reception robot. Our method produced distinctive task- and person-oriented dialogue styles, i.e., provided the predicted theory-based multi-modal communicative behaviors. The task-oriented style showed a more formal, shorter and less chatty communication. Overall, there was a rather smooth design convergence process, in which the individual designs were harmonized into the iterative design. For the selected design problem, the ID-tool had a satisfactory usability. Next steps include validation of the communication styles in an empirical study and, subsequently, identification of reusable design patterns.

This paper contributes to the automatic estimation of the subjective emotional experience that audio-visual media content induces in individual viewers, e.g. to support affect-based recommendations. Making accurate predictions of these responses is a challenging task because of their highly person-dependent and situation-specific nature. Findings from psychology indicate that an important driver for the emotional impact of media is the triggering of personal memories in observers. However, existing research on automated predictions focuses on the isolated analysis of audiovisual content, ignoring such contextual influences. In a series of empirical investigations, we (1) quantify the impact of associated personal memories on viewers' emotional responses to music videos in-the-wild and (2) assess the potential value of information about triggered memories for personalizing automatic predictions in this setting. Our findings indicate that the occurrence of memories intensifies emotional responses to videos. Moreover, information about viewers' memory response explains more variation in video-induced emotions than either the identity of videos or relevant viewer-characteristics (e.g. personality or mood). We discuss the implications of these results for existing approaches to automated predictions and describe ways for progress towards developing memory-sensitive alternatives.

To better understand the nature, function and elicitation conditions of emotion it is important to approach studying emotion from a multidisciplinary perspective involving psychology, neuroscience and affective computing. Recently, the TDRL Theory of Emotion has been proposed. It defines emotions as variations of temporal difference assessments in reinforcement learning. In this paper we present new evidence for this theory. We show that regret - a negative emotion that signifies that an alternative action should have been taken given new outcome evidence - is modelled by a particular form of TD error assessment. In our model regret is attributed to each action in the state-action trace of an agent for which - after new reward evidence - an alternative action becomes the best action in that state (the new argmax) after adjusting the action value of the chosen action in that state. Regret intensity is modeled as the difference between this new best action and the adjusted old best action, reflecting the additional amount of return that could have been received should that alternative have been chose. We show in simulation experiments how regret varies depending on the amount of adjustment as well as the adjustment mechanism, i.e. Q-trace, Sarsa-trace, and Monte Carlo (MC) re-evaluation of action values. Our work shows plausible regret attribution to actions, when this model of regret is coupled with MC action value update. This is important evidence that regret can be seen as a particular variation of TD error assessment involving counterfactual thinking.

An important aspect of human emotion perception is the use of contextual information to understand others' feelings even in situations where their behavior is not very expressive or has an emotionally ambiguous meaning. For technology to successfully detect affect, it must mimic this human ability when analyzing audiovisual input. Databases upon which machine learning algorithms are trained should capture the context of social interactions as well as the behavior expressed in them. However, there is a lack of consensus about what constitutes relevant context in such databases. In this article, we make two contributions towards overcoming this challenge: (a) we identify two principal sources of context for emotion perceptions based on psychological theory, and (b) we provide an overview of how each of these has been considered in published databases covering social interactions. Our results show that a similar set of contextual features are present across the reviewed databases. Between all the different databases researchers seem to have taken into account a set of contextual features reflecting the sources of context seen in psychological theory. However, within individual databases, these features are not yet systematically varied. This is problematic because it prevents them from being used directly as resources for the modeling of context-sensitive affect detection. Based on our findings, we suggest improvements for the future development of affective databases.

Explanation of actions is important for transparency of-, and trust in the decisions of smart systems. Literature suggests that emotions and emotion words - in addition to beliefs and goals - are used in human explanations of behaviour. Furthermore, research in e-health support systems and human-robot interaction stresses the need for studying long-term interaction with users. However, state of the art explainable artificial intelligence for intelligent agents focuses mainly on explaining an agent's behaviour based on the underlying beliefs and goals in short-term experiments. In this paper, we report on a long-term experiment in which we tested the effect of cognitive, affective and lack of explanations on children's motivation to use an e-health support system. Children (aged 6-14) suffering from type 1 diabetes mellitus interacted with a virtual robot as part of the e-health system over a period of 2.5 - 3 months. Children alternated between the three conditions. Agent behaviours that were explained to the children included why 1) the agent asks a certain quiz question; 2) the agent provides a specific tip (a short instruction) about diabetes; or, 3) the agent provides a task suggestion, e.g., play a quiz, or, watch a video about diabetes. Their motivation was measured by counting how often children would follow the agent's suggestion, how often they would continue to play the quiz or ask for an additional tip, and how often they would request an explanation from the system. Surprisingly, children proved to follow task suggestions more often when no explanation was given, while other explanation effects did not appear. This is to our knowledge the first longterm study to report empirical evidence for an agent explanation effect, challenging the next studies to uncover the underlying mechanism.

A mayor challenge in human-robot interaction and collaboration is the synthesis of non-verbal behaviour for the expression of social signals. Appropriate perception and expression of dominance (verticality) in non-verbal behaviour is essential for social interaction. In this paper, we present our work on algorithmic modulation of robot bodily movement to express varying degrees of dominance. We developed a parameter-based model for head tilt and body expansiveness. This model was applied to a variety of behaviours. These behaviours were evaluated by human observers in two different studies with respectively static pictures of key postures (N=772) and realtime gestures (N=31). Overall, specific behaviours proved to communicate different levels of dominance. Further, modulation of body expansiveness and head tilt robustly influenced perceived dominance independent of specific behaviours and observer viewing height and angle. The modulation did not influence perceived valence, but it did influence perceived arousal. Our study shows that dominance can be reliably expressed by both selection of specific behaviours and modulation of behaviours.

This paper presents the design and evaluation of human-like welcoming behaviors for a humanoid robot to draw the attention of passersby by following a three-step model: (1) selecting a target (person) to engage, (2) executing behaviors to draw the target's attention, and (3) monitoring the attentive response. A computer vision algorithm was developed to select the person, start the behaviors and monitor the response automatically. To vary the robot's enthusiasm when engaging passersby, a waving gesture was designed as basic welcoming behavioral element, which could be successively combined with an utterance and an approach movement. This way, three levels of enthusiasm were implemented: Mild (waving), moderate (waving and utterance) and high (waving, utterance and approach movement). The three levels of welcoming behaviors were tested with a Pepper robot at the entrance of a university building. We recorded data and observation sheets from several hundreds of passersby (N = 364) and conducted post-interviews with randomly selected passersby (N = 28). The level selection was done at random for each participant. The passersby indicated that they appreciated the robot at the entrance and clearly recognized its role as a welcoming robot. In addition, the robot proved to draw more attention when showing high enthusiasm (i.e., more welcoming behaviors), particularly for female passersby.

A mayor challenge in human-robot interaction is the synthesis of social signals through non-verbal behaviour expression. Appropriate perception and expression of dominance (verticality) is essential for social interaction. In this paper, we present our work on algorithmic modulation of robot bodily movement to control dominance expression. We developed a parameter-based model for body expansiveness. This model was applied to a variety of behaviours and evaluated by human observers in two different studies with respectively static postures (N=772) and gestures (N=31). Modulation of body expansiveness proved to robustly influence perceived dominance independent of behaviour and viewing angles.

This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents. The survey focuses on agent/robot emotions, and mostly ignores human user emotions. Emotions are recognized as functional in decision-making by influencing motivation and action selection. Therefore, computational emotion models are usually grounded in the agent’s decision making architecture, of which RL is an important subclass. Studying emotions in RL-based agents is useful for three research fields. For machine learning (ML) researchers, emotion models may improve learning efficiency. For the interactive ML and human–robot interaction community, emotions can communicate state and enhance user investment. Lastly, it allows affective modelling researchers to investigate their emotion theories in a successful AI agent class. This survey provides background on emotion theory and RL. It systematically addresses (1) from what underlying dimensions (e.g. homeostasis, appraisal) emotions can be derived and how these can be modelled in RL-agents, (2) what types of emotions have been derived from these dimensions, and (3) how these emotions may either influence the learning efficiency of the agent or be useful as social signals. We also systematically compare evaluation criteria, and draw connections to important RL sub-domains like (intrinsic) motivation and model-based RL. In short, this survey provides both a practical overview for engineers wanting to implement emotions in their RL agents, and identifies challenges and directions for future emotion-RL research.

Educational technology needs a model of learning goals to support motivation, learning gain, tailoring of the learning process, and sharing of the personal goals between different types of users (i.e., learner and educator) and the system. This paper proposes a tree-based learning goal structuring to facilitate personal goal setting to shape and monitor the learning process. We developed a goal ontology and created a user interface representing this knowledge-base for the self-management education for children with Type 1 Diabetes Mellitus. Subsequently, a co-operative evaluation was conducted with healthcare professionals to refine and validate the ontology and its representation. Presentation of a concrete prototype proved to support professionals' contribution to the design process. The resulting tree-based goal structure enables three important tasks: ability assessment, goal setting and progress monitoring. Visualization should be clarified by icon placement and clustering of goals with the same difficulty and topic. Bloom's taxonomy for learning objectives should be applied to improve completeness and clarity of goal content.

A good explanation takes the user who is receiving the explanation into account. We aim to get a better understanding of user preferences and the differences between children and adults who receive explanations from a robot. We implemented a Nao-robot as a belief-desire-intention (BDI)-based agent and explained its actions using two different explanation styles. Both are based on how humans explain and justify their actions to each other. One explanation style communicates the beliefs that give context information on why the agent performed the action. The other explanation style communicates the goals that inform the user of the agent's desired state when performing the action. We conducted a user study (19 children, 19 adults) in which a Nao-robot performed actions to support type 1 diabetes mellitus management. We investigated the preference of children and adults for goalversus belief-based action explanations. From this, we learned that adults have a significantly higher tendency to prefer goal-based action explanations. This work is a necessary step in addressing the challenge of providing personalised explanations in human-robot and human-agent interaction.

Social robots are entering the private and public domain where they engage in social interactions with nontechnical users. This requires robots to be socially interactive and intelligent, including the ability to display appropriate social behaviour. Progress has been made in emotion modelling. However, research into behaviour style is less thorough; no comprehensive, validated model exists of non-verbal behaviours to express style in human-robot interactions. Based on a literature survey, we created a model of non-verbal behaviour to express high/low warmth and competence - two dimensions that contribute to teaching style. In a perception study, we evaluated this model applied to a NAO robot giving a lecture at primary schools and a diabetes camp in the Netherlands. For this, we developed, based on expert ratings, an instrument measuring perceived warmth, competence, dominance and affiliation. We show that even subtle manipulations of robot behaviour influence children's perceptions of the robot's level of warmth and competence.

The technology to simulate emotions for NPCs is industry ready. This has been the case for several years, and recent scientific advancements show that pluggable, black-box, emotion engines are feasible. We believe that exposure to, and ease-of-use of such engines are important factors that limit uptake. To facilitate this, we have developed a fully documented JavaScript-based version of GAMYGDALA including a plugin for the JavaScript-based game engine Phaser. Further, to seed imagination, we have developed two experimental games: an emotional arcade game and an emotional puzzle game. The games use GAMYGDALA to simulate NPC emotions and showcase novel gameplay enabled by emotion simulation. Further, these two cases show how straightforward simulating emotions is when properly supported by an emotion engine.

Subliminal priming is an extensively researched technique in cognitive psychology. Research often focuses on highly controlled lab-environments, with only a few studies attempting to translate it to applications outside the laboratory. In this study, visual affect priming was deployed in the complex environment of a horror computer game, while maintaining strict standards in regard to subliminal thresholds. Fear-inducing images of one prime-type were shown repeatedly to players (N=60) during 5-minute playing sessions, using sandwich masking and a prime-duration of 33.3 ms. Three types of images were compared to an empty control-image: text, faces and spiders. Players were monitored with heart-rate and galvanic skin response (GSR) sensors to determine effects on a physiological level and were interviewed directly after playing. Results show no significant differences in affective self-report. GSR measures show an increase of relaxation between the start and finish of the game for players who were primed with face images, which we attribute to a result of our relative small player sample. We conclude that in a perceptually complex environment such as a video-game, subliminal visual priming does not noticeably influence player affect. However, measures directly around prime-windows coinciding with in-game sounds showed a significantly effect on GSR. This suggests that GSR is a suitable tool to gauge the affective impact of game elements.

This study proposes a homeostasis-based affective system with emotion and mood as a way to communicate a plant's health state and environmental needs to preschool and primary school children. A system is proposed that expresses mood and emotion to express the plant's health state and its affective reaction to user-induced environmental changes respectively. A long-term goal is to enhance empathic reasoning in children and respect for plants and life in general, using affect as a communicative interface (even though the underlying system is not emotive per se). A fundamental issue addressed in this work is to what extend it is useful to add affective communication to a system that is otherwise non-affective (plants in our case) in order to better understand that system's state. A computer simulation of the affective plant was tested (n=7). Our results suggest that children can identify the simulated plant's needs and state based on graphically expressed affect, and can act to enhance the homeostasis of the plant.

A parameterized behavior model was developed for robots to show mood during task execution. In this study, we applied the model to the coverbal gestures of a robotic storyteller. This study investigated whether parameterized mood expression can 1) show mood that is changing over time; 2) reinforce affect communication when other modalities exist; 3) influence the mood induction process of the story; and 4) improve listeners' ratings of the storytelling experience and the robotic storyteller. We modulated the gestures to show either a congruent or an incongruent mood with the story mood. Results show that it is feasible to use parameterized coverbal gestures to express mood evolving over time and that participants can distinguish whether the mood expressed by the gestures is congruent or incongruent with the story mood. In terms of effects on participants we found that mood-modulated gestures (a) influence participants' mood, and (b) influence participants' ratings of the storytelling experience and the robotic storyteller.

The aim of our work is to design bodily mood expressions of humanoid robots for interactive settings that can be recognized by users and have (positive) effects on people who interact with the robots. To this end, we develop a parameterized behavior model for humanoid robots to express mood through body language. Different settings of the parameters, which control the spatial extent and motion dynamics of a behavior, result in different behavior appearances expressing different moods. In this study, we applied the behavior model to the gestures of the imitation game performed by the NAO robot to display either a positive or a negative mood. We address the question whether robot mood displayed simultaneously with the execution of functional behaviors in a task can (a) be recognized by participants and (b) produce contagion effects. Mood contagion is an automatic mechanism that induces a congruent mood state by means of the observation of another person’s emotional expression. In addition, we varied task difficulty to investigate how the task load mediates the effects. Our results show that participants are able to differentiate between positive and negative robot mood and they are able to recognize the behavioral cues (the parameters) we manipulated. Moreover, self-reported mood matches the mood expressed by the robot in the easy task condition. Additional evidence for mood contagion is provided by the fact that we were able to replicate an expected effect of negative mood on task performance: in the negative mood condition participants performed better on difficult tasks than in the positive mood condition, even though participants’ self-reported mood did not match that of the robot.

In this paper we computationally study the relation between adaptive behaviour and emotion. Using the reinforcement learning framework, we propose that learned state utility, V(s), models fear (negative) and hope (positive) based on the fact that both signals are about anticipation of loss or gain. Further, we propose that joy/distress is a signal similar to the error signal. We present agent-based simulation experiments that show that this model replicates psychological and behavioural dynamics of emotion. This work distinguishes itself by assessing the dynamics of emotion in an adaptive agent framework – coupling it to the literature on habituation, development, extinction and hope theory. Our results support the idea that the function of emotion is to provide a complex feedback signal for an organism to adapt its behaviour. Our work is relevant for understanding the relation between emotion and adaptation in animals, as well as for human–robot interaction, in particular how emotional signals can be used to communicate between adaptive agents and humans.

This paper reports our investigation into the effects of bodily mood expression of a humanoid robot in a scenario close to real life. To this end, we used the NAO robot to perform as a lecturer in a university class. To display either a positive or a negative mood, we modulated 41 co-verbal gestures by adjusting behavior parameters that control spatial extent and motion dynamics, without modifying gesture function. Unique in this study is that (a) the robot gave an actual lecture to real students, (b) the interaction is one-to-many and relatively long (30 min), and (c) mood modulation was applied to a large set of behaviors. The robot presented the same lecture either in a positive or a negative mood to two audiences (between subjects). Although statistical analysis does not show that participants consciously recognized the robot mood, the results do show that participants in the positive mood condition rated their own arousal significantly higher than in the negative condition. Further, video annotation showed increased valence and arousal of the audience in the positive condition. Finally, participants' ratings of the lecturing quality and the gesture quality of the robot are higher in the positive condition, demonstrating the importance of robot mood expression in a one-to-many interaction setting.

Conflict is an omnipresent phenomenon in human society. It spans from individual decision-making trade-offs such as deciding what to do next (sleep, eat, work, play), to complex scenarios including politics and business. The social sciences, psychology, economy, and biology study the nature of conflict, its consequences, and strategies to successfully deal with it. Over the last decades computer science has joined those disciplines and studies conflict from a computational perspective. This special issue presents a selection of the best papers presented at the First Workshop of Conflict Resolution in Decision Making (COREDEMA). The workshop focused on computational approaches that tackle conflict in order to provide new insights and explore potential applications. The workshop was jointly hosted with the 12th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS) in Salamanca, Spain, from June 4 to 6, 2013.

In this paper we present GAMYGDALA, an emotional appraisal engine that enables game developers to easily add emotions to their Non-Player Characters (NPC). Our approach proposes a solution that is positioned between event coding of affect, where individual events have predetermined annotated emotional consequences for NPCs, and a full blown cognitive appraisal model. Instead, for an NPC that needs emotions the game developer defines goals and annotates game events with a relation to these goals. Based on this input, GAMYGDALA produces an emotion for that NPC according to the well-known OCC model. In this paper we provide evidence for the following: GAMYGDALA provides black-box Game-AI independent emotion support, is efficient for large numbers of NPCs, and is psychologically grounded.

Bodily expression of affect is crucial to human robot interaction. Our work aims at designing bodily expression of mood that does not interrupt ongoing functional behaviors. We propose a behavior model containing specific (pose and motion) parameters that characterize the behavior. Parameter modulation provides behavior variations through which affective behavioral cues can be integrated into behaviors. To investigate our model and parameter set, we applied our model to two concrete behaviors (waving and pointing) on a NAO robot, and conducted a user study in which participants (N=24) were asked to design such variations corresponding with positive, neutral, and negative moods. Preliminary results indicated that most parameters varied significantly with the mood variable. The results also suggest that the relative importance may be different between parameters, and parameters are probably interrelated. This paper presents the analysis of these aspects. The results show that the spatial extent parameters (hand-height and amplitude), the head vertical position, and the temporal parameter (motion-speed) are the most important parameters. Moreover, multiple parameters were found to be interrelated. These parameters should be modulated in combination to provide particular affective cues. These results suggest that a designer should focus on the design of the important behavior parameters and utilize the parameter combinations when designing mood expression.

Bodily expression of affect is crucial to human robot interaction. We distinguish between emotion and mood expression, and focus on mood expression. Bodily expression of an emotion is explicit behavior that typically interrupts ongoing functional behavior. Instead, bodily mood expression is integrated with functional behaviors without interrupting them. We propose a parameterized behavior model with specific behavior parameters for bodily mood expression. Robot mood controls pose and motion parameters, while those parameters modulate behavior appearance. We applied the model to two concrete behaviors - waving and pointing - of the NAO robot, and conducted a user study in which participants (N=24) were asked to design the expression of positive, neutral, and negative moods by modulating the parameters of the two behaviors. Results show that participants created different parameter settings corresponding with different moods, and the settings were generally consistent across participants. Various parameter settings were also found to be behavior-invariant. These findings suggest that our model and parameter set are promising for expressing moods in a variety of behaviors.

In this special section we report on the workshop Standards in Emotion Modeling held in August 2011, Leiden, The Netherlands. An important goal of this workshop was to identify challenges related to the development and evaluation of computational models of affective processes. These challenges were approached from a psychological and a computational modeling perspective. In this introduction we present a summary of the results of this week-long workshop. In addition to that, we are proud to present an invited contribution that proposes solutions to several of these challenges.

Two problems may arise when an intelligent (recommender) system elicits users’ preferences. First, there may be a mismatch between the quantitative preference representations in most preference models and the users’ mental preference models. Giving exact numbers, e.g., such as “I like 30 days of vacation 2.5 times better than 28 days” is difficult for people. Second, the elicitation process can greatly influence the acquired model (e.g., people may prefer different options based on whether a choice is represented as a loss or gain). We explored these issues in three studies. In the first experiment we presented users with different preference elicitation methods and found that cognitively less demanding methods were perceived low in effort and high in liking. However, for methods enabling users to be more expressive, the perceived effort was not an indicator of how much the methods were liked. We thus hypothesized that users are willing to spend more effort if the feedback mechanism enables them to be more expressive. We examined this hypothesis in two follow-up studies. In the second experiment, we explored the trade-off between giving detailed preference feedback and effort. We found that familiarity with and opinion about an item are important factors mediating this trade-off. Additionally, affective feedback was preferred over a finer grained one-dimensional rating scale for giving additional detail. In the third study, we explored the influence of the interface on the elicitation process in a participatory set-up. People considered it helpful to be able to explore the link between their interests, preferences and the desirability of outcomes. We also confirmed that people do not want to spend additional effort in cases where it seemed unnecessary. Based on the findings, we propose four design guidelines to foster interface design of preference elicitation from a user view.

Classically, cognition assumes that the underlying mechanisms of thinking are based on symbol manipulation processes. This assumption has several drawbacks, such as the issue of where symbols (representations) actually come from. To overcome this drawback, the interactivist approach proposes that representations emerge from the continuous interaction with the environment and subsequent anticipation of such interaction. This approach provides understanding of the nature of knowledge representation and reasoning in adaptive agents. We have used the interactivist approach as basis for an interaction-based computational model. Our model is embedded in an adaptive agent whose task it is to find food in a maze. Feedback about the agent’s