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, ), 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.

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.

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.

Artificial Intelligence (AI) systems, including intelligent agents, are becoming increasingly complex. Explainable AI (XAI) is the capability of these systems to explain their behaviour, in a for humans understandable manner. Cognitive agents, a type of intelligent agents, typically explain their actions with their beliefs and desires. However, humans also take into account their own and other's emotions in their explanations, and humans explain their emotions. We refer to using emotions in XAI as Emotion-aware eXplainable Artificial Intelligence (EXAI). Although EXAI should also include awareness of the other's emotions, in this work we focus on how the simulation of emotions in cognitive agents can help them self-explain their behaviour. We argue that emotions simulated based on cognitive appraisal theory enable (1) the explanation of these emotions, (2) using them as a heuristic to identify important beliefs and desires for the explanation, and (3) the use of emotion words in the explanations themselves.

Most explainable AI (XAI) research projects focus on well-delineated topics, such as interpretability of machine learning outcomes, knowledge sharing in a multi-agent system or human trust in agent’s performance. For the development of explanations in human-agent teams, a more integrative approach is needed. This paper proposes a perceptual-cognitive explanation (PeCoX) framework for the development of explanations that address both the perceptual and cognitive foundations of an agent’s behavior, distinguishing between explanation generation, communication and reception. It is a generic framework (i.e., the core is domain-agnostic and the perceptual layer is model-agnostic), and being developed and tested in the domains of transport, health-care and defense. The perceptual level entails the provision of an Intuitive Confidence Measure and the identification of the “foil” in a contrastive explanation. The cognitive level entails the selection of the beliefs, goals and emotions for explanations. Ontology Design Patterns are being constructed for the reasoning and communication, whereas Interaction Design Patterns are being constructed for the shaping of the multimodal communication. First results show (1) positive effects on human’s understanding of the perceptual and cognitive foundation of agent’s behavior, and (2) the need for harmonizing the explanations to the context and human’s information processing capabilities.

This paper presents a cognitive (belief-desire-intention based) agent that can self-explain its behaviour based on its goals and emotions. We implement a cognitive agent, embodied by a nao-robot or virtual avatar thereof, to play a quiz with its user. During the interaction the agent intelligently selects questions to optimally educate the user. We show how the simulation of emotions can be used to generate end-user explanations of the agent's behaviour. With this we provide a first proof of concept showing the value of using simulated emotions in addition to goals for generating agent behaviour explanations.

This paper describes ongoing work carried out in the European project PAL which will support childre in their diabetes self-management as well as assist health professionals and parents involved in the diabete regimen of the child. Here, we will focus on the construction of the PAL ontology which has been assemble from several independently developed sub-ontologies and which are brought together by a set of hand-writte interface axioms, expressed in OWL.We will describe in detail how the triple model of RDF has been extende towards transaction time in order to represent time-varying data. Examples of queries and rules involvin temporal information will be presented as well. The approach is currently been in use in diabetes camps.

We present the Affective Storyteller, a narrative generation framework that combines storytelling and emotion. With this framework we propose to address two main challenges in narrative generation: customization, and, reduced calculation time. Our solution is based on the fact that narrative generation in the Affective Storyteller is influenced by an analysis of the emotional patterns of the synthetic characters in the stories. These emotions are simulated using GAMYGDALA.