After the novelty effect wears off children need a new motivator to keep interacting with a social robot. Enabling children to build a relationship with the robot is the key for facilitating a sustainable long-term interaction. We designed a memory-based personalization strategy that safeguards the continuity between sessions and tailors the interaction to the child's needs and interests to foster the child-robot relationship. A longitudinal (five sessions in two months) user study (N = 46, 8-10 y.o) showed that the strategy kept children interested longer in the robot, fosters more closeness, elicits more positive social cues, and adds continuity between sessions.

Objectives: Children with cancer often experience sleep problems, which are associated with many negative physical and psychological health outcomes, as well as with a lower quality of life. Therefore, interventions are strongly required to improve sleep in this population. We evaluated interactive education with respect to sleep hygiene with a social robot at a pediatric oncology outpatient clinic regarding the feasibility, experiences, and preliminary effectiveness. Methods: Researchers approached children (8 to 12 years old) who were receiving anticancer treatment and who were visiting the outpatient clinic with their parents during the two-week study period. The researchers completed observation forms regarding feasibility, and parents completed the Children’s Sleep Hygiene Scale before and two weeks after the educational regimen. The experiences of children and parents were evaluated in semi-structured interviews. We analyzed open answers by labeling each answer with a topic reflecting the content and collapsed these topics into categories. We used descriptive statistics to describe the feasibility and experiences, and a dependent-samples t-test to evaluate the preliminary effectiveness. Results: Twenty-eight families participated (58% response rate) and all interactions with the robot were completed. The children and parents reported that they learned something new (75% and 50%, respectively), that they wanted to learn from the robot more often (83% and 75%, respectively), and that they applied the sleeping tips from the robot afterwards at home (54%). Regarding the preliminary effectiveness, children showed a statistically significant improvement in their sleep hygiene (p = 0.047, d = 0.39). Conclusions: Providing an educational regimen on sleep hygiene in a novel, interactive way by using a social robot at the outpatient clinic seemed feasible, and the children and parents mostly exhibited positive reactions. We found preliminary evidence that the sleep hygiene of children with cancer improved.

While interacting with a social robot, children have a need to express themselves and have their expressions acknowledged by the robot. A need that is often unaddressed by the robot, due to its limitations in understanding the expressions of children. To keep the child-robot interaction manageable the robot takes control, undermining children's ability to co-regulate the interaction. Co-regulation is important for having a fulfilling social interaction. We developed a co-creation activity that aims to facilitate more co-regulation. Children are enabled to create sound effects, gestures, and light shows for the robot to use during their conversation. Results from a user study (N = 59 school children, 7-11 y.o.) showed that the co-creation activity successfully facilitated co-regulation by improving children's agency. Co-creation furthermore increases children's acceptance of the robot.

Effective use of negotiation support systems depends on the systems capability of explaining itself to the user. This paper introduces the notion of an explanation matrix and an aberration detection mechanism for bidding strategies. The aberration detection is a mechanism that detects if one of the negotiating parties deviates from their expected behaviour, i.e. when a bid falls outside the range of expected behaviour for a specific strategy. The explanation matrix is used when to explain which aberrations to the user. The idea is that the user, when understanding the aberration, can take effective action to deal with the aberration. We implemented our aberration detection and our explanation mechanisms in the Pocket Negotiator (PN). We evaluated our work experimentally in a task in which participants are asked to identify their opponent’s bidding strategy, under different explanation conditions. As the number of correct guesses increases with explanations, indirectly, these experiments show the effectiveness of our aberration detection mechanism. Our experiments with over 100 participants show that suggesting consistent strategies is more effective than explaining why observed behaviour is inconsistent. An extended abstract of this article can be found in [15].

Background: Society is facing a global shortage of 17 million health care workers, along with increasing health care demands from a growing number of older adults. Social robots are being considered as solutions to part of this problem. Objective: Our objective is to evaluate the quality of care perceived by patients and caregivers for an integrated care pathway in an outpatient clinic using a social robot for patient-reported outcome measure (PROM) interviews versus the currently used professional interviews. Methods: A multicenter, two-parallel-group, nonblinded, randomized controlled trial was used to test for noninferiority of the quality of care delivered through robot-assisted care. The randomization was performed using a computer-generated table. The setting consisted of two outpatient clinics, and the study took place from July to December 2019. Of 419 patients who visited the participating outpatient clinics, 110 older patients met the criteria for recruitment. Inclusion criteria were the ability to speak and read Dutch and being assisted by a participating health care professional. Exclusion criteria were serious hearing or vision problems, serious cognitive problems, and paranoia or similar psychiatric problems. The intervention consisted of a social robot conducting a 36-item PROM. As the main outcome measure, the customized Consumer Quality Index (CQI) was used, as reported by patients and caregivers for the outpatient pathway of care. Results: In total, 75 intermediately frail older patients were included in the study, randomly assigned to the intervention and control groups, and processed: 36 female (48%) and 39 male (52%); mean age 77.4 years (SD 7.3), range 60-91 years. There was no significant difference in the total patient CQI scores between the patients included in the robot-assisted care pathway (mean 9.27, SD 0.65, n=37) and those in the control group (mean 9.00, SD 0.70, n=38): P=.08, 95% CI -0.04 to 0.58. There was no significant difference in the total CQI scores between caregivers in the intervention group (mean 9.21, SD 0.76, n=30) and those in the control group (mean 9.09, SD 0.60, n=35): P=.47, 95% CI -0.21 to 0.46. No harm or unintended effects occurred. Conclusions: Geriatric patients and their informal caregivers valued robot-assisted and nonrobot-assisted care pathways equally.

The issue of explainability for autonomous systems is becoming increasingly prominent. Several researchers and organisations have advocated the provision of a “Why did you do that?” button which allows a user to interrogate a robot about its choices and actions. We take previous work on debugging cognitive agent programs and apply it to the question of supplying explanations to end users in the form of answers to why-questions. These previous approaches are based on the generation of a trace of events in the execution of the program and then answering why-questions using the trace. We implemented this framework in the agent infrastructure layer and, in particular, the Gwendolen programming language it supports – extending it in the process to handle the generation of applicable plans and multiple intentions. In order to make the answers to why-questions comprehensible to end users we advocate a two step process in which first a representation of an explanation is created and this is subsequently converted into natural language in a way which abstracts away from some events in the trace and employs application specific predicate dictionaries in order to translate the first-order logic presentation of concepts within the cognitive agent program in natural language. A prototype implementation of these ideas is provided.

The commercial availability of robots and voice-operated smart devices such as Alexa or Google Home have some companies wondering whether they can replace some current human interactions by using these devices. One such area of interaction is at the reception desk. While both platforms can offer the necessary interaction features to take on the task of an automated receptionist, the question remains as to which platform actual visitors would prefer - body or no body? To this end we created a receptionist agent that can receive visitors with an appointment, presented as either an embodied robot or a disembodied smart display. The agent uses common commercial products and services, and was tested in a real-world environment with real visitors. The results show no significant difference in visitor preference for either platform.

In this paper we specify and validate three interaction design patterns for an interactive storytelling experience with an autonomous social robot. The patterns enable the child to make decisions about the story by talking with the robot, reenact parts of the story together with the robot, and recording self-made sound effects. The design patterns successfully support children's engagement and agency. A user study (N = 27, 8-10 y.o.) showed that children paid more attention to the robot, enjoyed the storytelling experience more, and could recall more about the story, when the design patterns were employed by the robot during storytelling. All three aspects are important features of engagement. Children felt more autonomous during storytelling with the design patterns and highly appreciated that the design patterns allowed them to express themselves more freely. Both aspects are important features of children's agency. Important lessons we have learned are that reducing points of confusion and giving the children more time to make themselves heard by the robot will improve the patterns efficiency to support engagement and agency. Allowing children to pick and choose from a diverse set of stories and interaction settings would make the storytelling experience more inclusive for a broader range of children.

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.

Patient reported outcome measures (PROMs) are an essential means for collecting information on the effectiveness of hospital care as perceived by the patients themselves. Especially older adult patients often require help from nursing staff to successfully complete PROMs, but this staff already has a high work load. Therefore, a social robot is introduced to perform the PROM questioning and recording task. The study objective was to design a multimodal dialogue for a social robot to acquire PROMs for older patients. The primary outcomes were the effectiveness, the efficiency, and the subjective usability as perceived by older adults of acquiring PROMs by a social robot. The robot dialogue design included a personalized welcome, PROM questions, confirmation requests, affective statements, use of a support screen on the robot displaying the answer options, and accompanying robot gestures. The design was tested in a crossover study with 31 community-dwelling persons aged 70 years or above. Answers obtained with the robot were compared with those obtained by a questionnaire taken by humans. First results indicated that PROM data collection in older persons may be carried out effectively and efficiently by a social robot. The robot’s subjective usability was on average scored as 80.1 (± 11.6) on a scale from 0 to 100. The recorded data reliability was 99.6%. A first relevant step has been made on the design trajectory for a robot to obtain PROMs from older adults. Practice variation in subjective usability scores still asks for technical dialogue improvements.

Drawing the attention of passersby is a basic task of a social robot to initiate an interaction in a public environment (e.g., shopping malls, museums or hospitals). Humans use several social cues, both verbal and nonverbal, to draw the attention of others. In this study, we investigate whether similar behaviors can also be effectively used by a social robot for drawing attention. To this end, we setup a humanoid robot (Pepper) to act as a welcoming robot at the entrance of a university building. The behaviors selected for Pepper include one or a combination of behavioral modalities (i.e., a waving gesture, utterance and movement). These behaviors are triggered automatically using the output of people detection software which tracks passersby and monitors their head keypoints (nose, eyes, and ears). The reactions of people toward Pepper are observed and recorded by means of an observation sheet. For several weeks, we deployed Pepper at the entrance with the aim of wearing off the novelty effect. In our final study, we collected data from several hundreds of passersby N=364 and conducted post-interviews with randomly selected ones N=28. Passersby noticed Pepper at the entrance and clearly recognized its role as a welcoming robot. In addition, Pepper was able to draw more attention when displaying a combination of behavioral modalities. However, passersby did not recall the robot utterance as they, for example, were unable to reproduce it or mistakenly claimed that the robot said something when it was only waving.

We are developing a social robot that should autonomously interact long-term with pediatric oncology patients. The child and the robot need to get acquainted with one another before a long-term interaction can take place. We designed five interaction design patterns and two sets of robot behaviors to structure a getting acquainted interaction. We discuss the results of a user study (N = 75, 8–11 y.o.) evaluating these patterns and robot behaviors. Specifically, we are exploring whether the children successfully got acquainted with the robot and to what extent the children bonded with the robot. Results show that children effectively picked up how to talk to the robot. This is important, because the better the performance the more comfortable the children are, the more socially attractive the robot is, and the more intimate the conversation gets. The evaluation furthermore revealed that it is important for children, in order to get familiar with the robot, to have shared interests with the robot. Finally, most children did initiate a bond with the robot.

Background /Objectives: Healthcare professionals (HCP) are confronted with an increased demand for assessments of important health status measures, such as patient-reported outcome measurements (PROM), and the time this requires. The aim of this study was to investigate the effectiveness and acceptability of using an HCP robot assistant, and to test the hypothesis that a robot can autonomously acquire PROM data from older adults. Design: A pilot randomised controlled cross-over study where a social robot and a nurse administered three PROM questionnaires with a total of 52 questions. Setting: A clinical outpatient setting with community-dwelling older adults. Participants: Forty-two community-dwelling older adults (mean age: 77.1 years, SD: 5.7 years, 45% female). Measurements: The primary outcome was the task time required for robot-patient and nurse-patient interactions. Secondary outcomes were the similarity of the data and the percentage of robot interactions completed autonomously. The questionnaires resulted in two values (robot and nurse) for three indexes of frailty, well-being and resilience. The data similarity was determined by comparing these index values using Bland-Altman plots, Cohen's kappa (κ) and intraclass correlation coefficients (ICC). Acceptability was assessed using questionnaires. Results: The mean robot interview duration was 16.57 min (SD=1.53 min), which was not significantly longer than the nurse interviews (14.92 min, SD=8.47 min; p=0.19). The three Bland-Altman plots showed moderate to substantial agreement between the frailty, well-being and resilience scores (κ =0.61, 0.50 and 0.45, and ICC=0.79, 0.86 and 0.66, respectively). The robot autonomously completed 39 of 42 interviews (92.8%). Conclusion: Social robots may effectively and acceptably assist HCPs by interviewing older adults.

To improve a negotiator's ability to recognise bidding strategies, we pro-actively provide explanations that are based on the opponent's bids and the negotiator's guesses about the opponent's strategy. We introduce an aberration detection mechanism for recognising strategies and the notion of an explanation matrix. The aberration detection mechanism identifies when a bid falls outside the range of expected behaviour for a specific strategy. The explanation matrix is used to decide when to provide what explanations. We evaluated our work experimentally in a task in which participants are asked to identify their opponent's strategy in the environment of a negotiation support system, namely the Pocket Negotiator (PN). We implemented our explanation mechanism in the PN and experimented with different explanation matrices. As the number of correct guesses increases with explanations, indirectly, these experiments show the effectiveness of our aberration detection mechanism. Our experiments with over 100 participants show that suggesting consistent strategies is more effective than explaining why observed behaviour is inconsistent.

The evaluation of cognitive agent systems, which have been advocated as the next generation model for engineering complex, distributed systems, requires more benchmark environments that offer more features and involve controlling more units. One issue that needs to be addressed time and again is how to create a connector for interfacing cognitive agents with such richer environments. Cognitive agents use knowledge technologies for representing state, their actions and percepts, and for deciding what to do next. Issues such as choosing the right level of abstraction for percepts and action synchronization make it a challenge to design a cognitive agent connector for more complex environments. The leading principle for our design approach to connectors for cognitive agents is that each unit that can be controlled in an environment is mapped onto a single agent. We design a connector for the real-time strategy (RTS) game StarCraft and use it as a case study for establishing a design method for developing connectors for environments. StarCraft is particularly suitable to this end, as AI for an RTS game such as StarCraft requires the design of complicated strategies for coordinating hundreds of units that need to solve a range of challenges including handling both short-term as well as long-term goals. We draw several lessons from how our design evolved and from the use of our connector by over 500 students in two years. Our connector is the first implementation that provides full access for cognitive agents to StarCraft: Brood War.

Nowadays, off-the-shelf social robots are used more frequently by the HRI community to research social interactions with different types of users across a range of domains such as education, retail, health care, public places and other domains. Everyone doing HRI research with end-users is invited to submit a case study to our workshop. We are particularly interested in case studies where things did not go as planned. Case studies describing research in the lab or in the wild are both welcome. Examples of unplanned experiences could include, but are not limited to, unexpected responses from the user, issues with the experimental setup or simply having challenges with transferring theory to the real world. In this workshop, we focus on off-the-shelf robots. In order to generalize and compare differences across multiple HRI domains and create common solutions, we will provide a template for your case study. We are interested in learning how such unexpected HRI results can be reported. In the workshop, we will discuss and study how failures are reported and be inspired to create a list of good ways to report failures, which can hopefully be inspiring for the HRI community.