Escape rooms are multi-player games that contain several puzzles that need to be solved in order to open locked chests and discover new clues, which eventually enables the players to escape the room. While the players are inside the escape room, the game host observes the group through live cameras. When players tend to fail to make it out of the escape room in time, the host needs to give them hints to keep them on track. Popup-escape is a company that designs escape rooms. They have asked us to develop an application that supports the game host in the process of observing escape rooms. Hence, we developed an application that displays live video streams and shows valuable information about the progress of the game. The game host can configure the escape room in the application before players enter the escape room. This configuration sets up how the escape room is structured. The game host indicates the number of chests (key points in the game) that need to be unlocked and the time it should take players to open it. The application then processes the incoming video streams and detects chests that have been opened, as well as the level of current activity. The progress is measured against time. When the progress made is falling short compared to the preconfigured time limits, the host gets a warning, alerting him that the players in the escape room need a hint in order to be able to finish the game in time.

Helpline counselors can be trained faster using agent-based social skills training systems. These systems utilize conversational agents that simulate interactions with users and provide feedback. This research evaluates the noticeability of behavior changes in such a conversational agent. By examining participants' ability to detect and interpret these changes, valuable insights can be gained to enhance the agent's representation of real-life scenarios and improve its effectiveness as a training tool. A mixed-methods approach was employed, combining quantitative data using descriptive statistics and qualitative data through content analysis. The quantitative analysis revealed a precision score of 93% and a recall score of 36%, indicating a higher accuracy in correctly identifying behavior changes compared to capturing all instances of change. The qualitative analysis revealed that participants' perceptions fell within four distinct categories: communication style, positive emotion, negative emotion, and tone & attitude. Participants demonstrated heightened attentiveness to changes in positive behavior, particularly instances of happiness expressed by the agent, and displayed less attention towards changes in negative behavior. In conclusion, this research demonstrates that behavior changes in the conversational agent are noticeable, albeit with varying degrees of attention across different types of behavior. Further exploration with a larger sample size is warranted to ascertain the extent of noticeability. The diverse range of behavior changes indicates the agent's capability to adapt and exhibit visible shifts in behavior. These insights can inform the refinement and development of agent-based training systems, ultimately enhancing the training experiences of chat-based helpline counselors.

Seeing the virtual coach as a friend is beneficial as it increases the motivation, confidence, and perseverance of the accompanying. Here, an investigation has taken place to what characteristics the virtual coach must possess to establish this friendly relationship. Thus, the main research question is: What are the reasons for seeing the virtual coach as a stranger or friend? This research made use of pre-gathered data. Here, five hundred participants interacted with the text-based virtual coach Sam in five separate sessions. Afterwards, each participant rated the relationship with the virtual coach, followed by an explanatory free-text response to which thematic analysis was applied. This resulted in five main themes: Relation, Positive Characteristics, Perception, Impersonal, and Chat Opinions. These were used to determine the characteristics the virtual coach must have to be considered a friend. Furthermore, these themes were used to obtain correlations. With these, it has been concluded that the user is more likely to develop a closer relationship with the virtual coach when: perceiving it as a human, pleasantly conversing, and being positive about its character. Whereas the user's age has a negligible influence on the relationship.

Women are largely underrepresented in IT, girls’ interest in STEM and IT fields tends to drop throughout secondary education. Educational games are a great tool to change the perception of certain topics, as well as changing the behavior of the players. Thus, this report describes the development of a game to make the field of IT more appealing to girls between the ages of 10 and 14. After collecting requirements with the client and doing a literature study a design is proposed. The final product is a two-player 2D Role-Playing-Game with puzzle elements, specifically designed to be played in a classroom environment. The game takes place in a data center and will show the players the societal importance of data centers as well as the diversity of the work in data centers. The gameplay consists of exploring a data center, talking with both male and female employees in various roles, helping them with their work through minigames, and solving a mystery. The game was designed to specifically cater to girls and to break stereotypes regarding women in IT.

A virtual coach, Sam, was created by the Perfect Fit project to aid users in establishing a foundation to stop smoking via the means of making them more physically active. Data from 500 users had been collected from them during stages which start with a pre-screening phase until a post-questionnaire phase. In the latter stage, they provided a rating regarding their satisfaction or dissatisfaction with Sam and a free-text response mentioning the reasons why. Through different means of analysing the data, the research question is answered, which is determining the reasons to be satisfied or dissatisfied with a virtual coach for quitting smoking and becoming more physically active. The three methods of analysis that have been used are a qualitative thematic analysis of the free-text responses, a quantitative analysis of user characteristics and ratings to obtain Pearson correlations, and a literature study. The findings display five themes which altered users' satisfaction when using Sam. These themes are technical competency, beneficiality, user experience, interrelations and conversationalist. The quantitative analysis had resulted in correlations of at most a moderate degree. However, from the ones which displayed a significant p-value, only 3 correlations against ratings were found and 3 correlations against themes were found. A literature study conducted on outcomes of research from relevant eHealth applications aided the direction of some correlations, such as ones with personality traits. Moreover, their correlation outcomes were compared to shed light on whether the outcomes of this research and previous ones align or differ. Some correlations did align such as with the case of the beneficiality theme against a user's characteristic of conscientiousness and emotional stability.

The aim of this research is to evaluate the believability of Lilobot, a conversational agent meant to act as a virtual child for training helpline workers. Numerous aspects of believability are explored by means of a user study involving a questionnaire and interview with 10 participants. Questionnaire results indicate that improvement to the chatbot's believability is likely necessary. The findings from the interviews are that the use of emoticons and acknowledging the context of the application raise believability, while unresponsiveness and repeated utterances lower it. While Lilobot did express valid and real emotions, study participants suggested improving the appropriateness of its reactions and expanding its vocabulary.

Chatbots are tools that can potentially be utilized in chat-based child helpline training. In this type of training, the quality of the feedback received is of vital importance. This paper aims to analyze the automated feedback generated by such a chatbot. The domains analyzed include user comprehension, usefulness, and potential improvement points. In a user study, a formative assessment and two interviews were conducted for each domain, respectively. For comprehensibility, all participants could easily understand the feedback report. They found that the bot could be easier to work around after reading the feedback, with the table being of much guidance. They found the transcript to be a welcome addition, but missing constructive feedback. Regarding improvement points, two of them were tightly related to the limitations of the chatbot, rather than the report itself. Extra guidance and instructions were deemed necessary by the participants, alongside an easier-to-read transcript interface.

To assist smokers in potentially quitting their habit, this paper investigates digital eHealth applications. Based on a dataset provided by research into an eHealth application, it aims to determine if persuasive activities can convince users of the usefulness of competencies determined to assist in quitting smoking. A thematic analysis was used on the participant's qualitative responses to the activities. Using this and the quantitative data derived, insights into the efficacy of persuasion were determined. It was determined that engagement with the optional qualitative aspect of the data produced similar utility perspectives on the competencies to those who did not comment. It was noted that the general perspective of the competencies rose after completing the activity, however not to a significant degree. Additionally, no notable correlations between age, gender or educational level and increased perception of the competency arose. Several interesting remarks from participants were analysed to offer considerations for any future research in this field.

Artificial social agents (ASAs) are systems designed to interact with humans in a socially intelligent manner. As the field of robotics is rapidly advancing, some studies focused on creating more effective agents by analysing how people perceive them. However, culture affects people’s perception of ASAs. So, cultural aspects must be examined in order to create more effective ASAs. This study aims to contribute to the research of cultural influence on ASAs by discovering the cultural similarities and differences in the perception of ASAs from Dutch and Chinese speakers. An English questionnaire has been made to evaluate ASAs and also has been translated into Chinese. In this study, the questionnaire is translated into Dutch and validated using both the original and the translated questionnaire. While assessing the translation, data is gathered from Dutch speakers on a human-ASA interaction. This data is then compared to the previously collected data from the Chinese translation study by calculating their mean, standard deviation and t-distribution of the differences between both cultural groups. On an item level, the results show a satisfactory level of correlation (ICC M = 0.67, SD = 0.12, range [0.36, 0.92]). On a construct/dimension level, results show a good level of correlation (ICC M = 0.80, SD = 0.11, range [0.52, 0.93]). Correction values for the translation have been advised for converting item scores between the two questionnaires. Cultural differences have been found and reported between Dutch and Chinese speakers, which could be used in future research on creating more effective ASAs.

A significant cardiovascular health risk is insufficient physical activity. The World Health Organization recommends 150 minutes of strenuous physical activity every week. Inadequate physical activity increases the risk of chronic diseases and other health conditions like cholesterol and obesity. This thesis researches the role of data monitoring as a persuasion strategy in monitoring a user’s progress in their journey to becoming more physically active and how it can be leveraged to decrease the risk of cardiovascular diseases. Specifically, the focus of the thesis is to determine the effectiveness of expert-generated tailored messages to motivate a user in their physical activity behaviour. We designed the content of the messages by adapting an existing ontology for tailoring motivational messages in the context of physical activity. Messages were then generated by experts through a scenario-based feedback generation process, where the scenarios were tailored to a user’s mood, self-efficacy and progress. The design of these tailored messages was tested against generic messages to determine which type of message was more motivating to the user. An experiment was conducted by recruiting crowd workers who were asked to rate the motivational levels of the two message types with respect to a given scenario. The results of the experiment supported the initial hypothesis that messages tailored to mood, self-efficacy and progress are more motivating than generic messages. Additionally, we have shown a systematic and reproducible way to obtain motivating messages. We have also provided a dataset of motivational messages that can be used during various stages of a user’s physical activity intervention, along with a set of scenarios representing different levels of a user’s state (mood, self-efficacy and progress).

The phenomenon of one element moving and progressively overlaying another is common in nature, such as waves swashing and backwashing, or eyelids moving over eyeballs while blinking. Folding Texture, which was proposed by Thorben, can simulate this texture “folding” visual effect in real-time without changing geometry. However, to date, no tool has been developed to assist in the design and synthesis of folding textures. Applications of the technique so far are achieved through manual creation of the folding texture, which is a tedious process. This thesis explores the problem of folding-texture design and synthesis. A novel approach is proposed for animating still images based on the folding texture technique. The approach uses a semi-automatic, user-assisted method that combines texture editing, motion profile specification, and folding texture synthesis into one seamless process, reducing the need for extensive manual work. It enables novice users to utilize the technique with a fair level of prior knowledge of folding texture.

Dementia care is a growing problem, both due to a rising number of cases and due to a shortage in healthcare workers. Aside from cognitive symptoms persons with dementia (PwDs) often deal with psychological symptoms such as agitation. The individualized music intervention (IMI) by Linda Gerdner has been proposed to reduce these. This is the basis for our first research question “How can we incorporate the IMI into technology to care for PwDs?". We answer this by dividing the guidelines for IMI into functional components with their own responsibilities. This allows for more independent development of these components. We also research one of the components, a playlist generator. This leads us to our second research question "Assuming knowledge of a list of preferred music for a user, can we generate playlists which better regulate emotions than a random shuffle?". Our generator is based on a Markov Decision Process (MDP) and allows for a target to be set in the 2D valence-arousal space. This was tested in an online experiment with 22 participants. The control group listened to a random shuffle of their preferred music. The experimental group listened to a playlist generated by the MDP. The target was set to a valence > 0.75 (valence ranging from 0 to 1). A significantly higher final valence for the experimental group was observed, showing a clear improvement over the control group when the goal is to positively influence the valence. The dataset obtained through this experiment is available for future research.

Reading is an essential skill for any child to learn, and finding enjoyment in it can greatly contribute to developing proper reading comprehension. Finding the books they like could prove to be difficult. Utilizing collaborative filtering recommender systems to recommend books to children is a tricky task, the lack of user feedback makes it difficult to accurately recommend books they would enjoy. Using content based recommender systems might be preferable, but what book features could a recommender system like this base recommendations on? This research explores the idea of utilizing the textual complexity of books and their descriptions as such a possible feature. By evaluating how accurate readability formulas can predict the age a book is intended for, how the variability and length of sentences vary per age and analysing the difficulty of words used, this paper finds that the descriptions of books intended for younger audiences might not be aimed at them, but instead at their parents. These findings imply that basing recommendations of the textual complexity of book descriptions might not be the most useful feature to base recommendations of.

As guided interventions are seen as more effective compared to unguided interventions and because the use of eHealth applications is rising over the years, the need to study the effects of a virtual coach as a substitute for guided interventions was created. A virtual coach does not cost as much time and money as a real coach and would thus be beneficial in combination with a virtual smoking cessation application. Therefore, the reasons for preferring to use a questionnaire or virtual coach were studied in this research. Through three questionnaires, 500 participants have indicated their experience with a virtual coach Sam, helping them to prepare to quit smoking. Based on questions about their preference for a virtual coach or a questionnaire, seven themes have been identified. Most of the themes were connected to making the activities proposed by the virtual coach more appealing and therefore the intervention more effective. All in all, the most important aspects of accepting a virtual coach for smoking cessation had to do with the proposed content by the virtual coach.

Agent-based social skills training systems have been gaining attention for their potential to improve social skills development in various contexts. Through a rapid review methodology, data was collected from diverse sources, including company websites and research papers. This study then uses the collected data to categorize 8 commercial systems based on their agent model and feedback approaches, into two categorization tables. The findings reveal notable trends in the use of choice-based input, scenario-defined decision-making, and post-interaction feedback. Additionally, the paper discusses the limitations of these findings, highlights characteristics of commercial systems and compares them to research systems, as well as suggesting areas for future research. This study contributes to the understanding and advancement of agent-based social skills training systems, offering guidance to researchers in this field.

We analyze the internal representations that deep Reinforcement Learning (RL) agents form of their environments and whether these representations correspond to what such agents should ideally learn. The purpose of this comparison is both a better understanding of why certain algorithms or network architectures perform better than others and the development of methods that specifically target discrepancies between what is and what should be learned. The concept of ideal representation we utilize is based on stochastic bisimulation and bisimulation metrics, which are measures of whether and to which degree states are behaviorally similar, respectively. Learning an internal representation in which states are equivalent if and only if they are bisimilar and in which distances between non-equivalent states are proportional to how behaviorally similar the states are has several desirable theoretical properties. Yet, we show empirically that the extent to which such a representation is learned in practice depends on several factors and that a precise such representation is not created in any case. We further provide experimental results that suggest that learning a representation that is close to this target internal state representation during training may improve upon the learning speed and consistency, and doing so by the end of training upon generalization.

Het LUMC, het Leids Universitair Medisch Centrum, verzocht om een virtuele coach te ontwikkelen voor patiënten met hart- en vaatziekten. De coach zou deze patiënten thuis kunnen helpen om hen aan hun medicatie te houden. Daarvoor zou de patiënt de coach dagelijks kunnen opzoeken om zo feedback en motivatie te ontvangen van de coach. Ook zou de coach moeten kunnen helpen met veranderingen in levensstijl bevorderen. Na veel onderzoek gedaan te hebben naar dit probleem en de benodigdheden hiervoor, zijn de volgende twee producten als oplossing bedacht. Ten eerste is er een webapplicatie gemaakt waarbij een virtuele 3d coach vragen stelt en beantwoordt over verschillende onderwerpen. Ten tweede is er een editor in Java gemaakt. Met deze editor is het mogelijk om de verschillende onderwerpen en vragen van de coach te creëren en aan te passen. Deze methode staat toe dat experts op het gebied van hart- en vaatziekten de inhoud van de coach kunnen schrijven. Ook zullen hierdoor veranderingen in medicatie gemakkelijk doorgegeven kunnen worden aan de coach.

Police officers are exposed to many potentially traumatising and stressful situations, but they do not always find the right mental health help they need. In this thesis, conversational agent Robyn is developed to help officers keep an eye on their mental well-being and find help when needed. An evaluation approach is also presented. The conversational agent applies the Stress First Aid method to determine stress indicators together with the officer. The design of the agent followed the guidelines for Stress First Aid. The agent uses text input and output and went through an iterative design process using expert feedback. The experts provided feedback on both text and structure of the conversation. Based on their input, informal language was chosen for the presentation of the agent. The lessons for similar projects drawn from this design process are managing expectations from users, scoping broad questions, getting feedback from users on wording and not to push people when discussing suicide. The proposed evaluation would determine if the agent is effective at improving people's awareness of their mental well-being status. This is done by comparing perceived mental well-being before and after interaction to reported mental well-being. To achieve this, a survey to measure perceived mental well-being was developed. The evaluation also provides insight in what aspects of the agent need the most improvement in future iterations.

People with diabetes often show symptoms heavily associated with depression, but these symptoms are often caused by the burden of daily diabetes management. The negative feelings caused by this burden are defined as "diabetes burnout''. Some of these negative feelings are caused by social issues. People with diabetes often resort to online sources to find out how to deal with these social issues since health care providers do not focus on this side of diabetes. Furthermore, the social stigma surrounding treatment for mental problems stops people with diabetes from going to a psychologist. In this project, a conversational agent is designed, implemented and evaluated to investigate whether it is capable of reducing social diabetes distress. The agent was designed to give personalized tips based on a social issue the person with diabetes shares. A longitudinal experiment was done over three sessions to evaluate the agent. The results show that the agent is able to reduce the diabetes distress more than a plain textual delivery of tips. The successful application shows the value of conversational agents and provide a basis to deploy such conversational agents in the e-mental health domain. The design we created can be used in future work, where a further personalized approach and a tool measuring the personalization should be investigated in order to better understand why the conversational agent is able to reduce the diabetes distress.

Open Relation Extraction (ORE) aims to find arbitrary relation tuples between entities in unstructured texts. Even though recent research efforts yield state-of-the-art results for the ORE task by utilizing neural network based models, these works are solely focused on the English language. Methods were proposed to tackle the ORE task for multiple languages, yet these works fail to exploit relation patterns that are consistent over languages. Moreover, they require additional data to train translators, hindering efficient extension to new languages. In this work, we introduce a Language-consistent Open Relation Extraction Model (LOREM). By adding a language-consistent component to the current state-of-the-art open relation extraction model, we enable exploitation of information from multiple languages. Since we remove all dependencies on language-specific knowledge and external NLP tools such as translators, it is relatively easy to extend our model to new languages. An extensive evaluation performed on 5 languages shows that LOREM outperforms state-of-the-art monolingual and cross-lingual open relation extractors. Moreover, experiments on low- and even no-resource languages indicate that LOREM generalizes to other languages than the languages that it is trained on.