This thesis proposes and develops an interface and model in which advanced optimisation for general employee scheduling is made available to non-experts in computer science or optimisation. The interface teaches, guides, configures, dynamically creates a constraint programming (CP) model, iteratively improves, decreases black box properties, increases trust in the outcome, and complies with relevant European Union Artificial Intelligence law. The objective of this study is to allow a wider range of organisations to take advantage of CP techniques, with the potential to greatly improve efficiency, reduce unfairness, meet company goals, and improve employee satisfaction.

Employees are assigned to personalised shifts based on the expected demands of departments within an organisation, these are set by the domain expert in the field of employee scheduling. Next, to tailor the model to the organisation’s needs, the domain expert is guided in setting both the restrictive assumptions and priorities of shift assignment. To optimise the generated optimal schedule, it is encouraged to create and compare multiple sets of configurations and subsequent schedules. Historical demand data, organisation structure, contract information, and employee preferences are included. Multiple visual design iterations have been made, after which a working interface has been developed and improved iteratively in conjunction with experts in the field. Validations with external domain experts from various industries and organisations have shown that the interface performed effectively in supporting the objectives. Further research can be done to improve the speed of solving, implement diversity of solutions, support for highly custom constraints based on natural language, or interface reusability for other optimisation problems.

This study delves into machine learning (ML) education by conducting a comprehensive literature review, a targeted survey of ML lecturers in Dutch universities, and a comparative experiment. These methods aid in addressing the challenges of aligning teaching methods with the evolving nature of ML and the growing demands of the field, and fill in knowledge gaps on the success of different teaching methods in ML education. The paper investigates whether traditional methods are effective in equipping future engineers with the necessary skills for tomorrow's challenges, amidst the rapid advancement of ML and its applications. The literature review explores the range of teaching methods in ML education and not only, emphasizing a shift towards technology-enhanced and active learning approaches when teaching ML. A survey of ML lecturers explores the landscape of ML education in Dutch universities. The study investigates teaching methodologies, tools, and challenges, providing valuable insights into the evolving practices of ML instruction. Findings indicate a predominant trend towards adopting a blended approach, with lectures, projects, and group work forming core instructional methods. Virtual environments, active learning strategies, and staying informed through community engagement are highlighted. Word frequency and thematic analyses reveal key themes, emphasizing student-centric learning, practical application, and the integration of diverse teaching methods. Additionally, an experimental comparison of two teaching methods, lecture and jigsaw, sheds light on their seemingly similar efficacy when applied to the domain of ML education. The research contributes to the optimization of ML education practices, offering comprehensive insights for educators and policymakers.

This research investigates the impact of goal-oriented visualization on machine learning knowl-edge acquisition, particularly exploring its poten-tial to address procrastination in academic settings. By examining participants with no prior machine learning experience, the study employs compara-tive and correlational measures to analyze quiz per-
formance, study times, and visualization practices. While encouraging trends are observed, the small sample size emphasizes the need for further re-search to definitively establish the impact of visu-alization on academic performance. The study ad-heres to ethical guidelines, ensuring participant pri-vacy and obtaining informed consent, contributing to responsible research practices.

To aid the teachings of machine learning (ML), the usage of elaborative interrogative practice questions (EIPQ) is proposed to increase the long-term memory retention of said teaching. Firstly, the existing expectations of students in the current educational landscape are analyzed by taking a look at the undergraduate course present in Delft University of Technology's own Computer Science and Engineering program (CSE2510). Then, relevant theories and techniques for long-term memory retention through practice questions are introduced and applied to CSE2510 content. Finally, an experiment was carried out where roughly half of the participants made use of these newly created EIPQ, while the other half mostly used existing questions, serving as a control group (CQ). The results showed that, compared to the existing practice questions, the use of the newly created EIPQ had a profound impact on the long-term knowledge retention of the learning content. The participants who made use of EIPQ had an average retention ratio of 0.82, compared to the participants who made use of CQ, who had an average retention ratio of 0.57. Therefore, it is suggested that including EIPQ in our current educational model has favorable benefits to the students' knowledge retention of the learned content. A recommendation is made on how to carry out these methods in practice, keeping compatibility with existing learning objectives in mind.

With the prevalence of artificial intelligence recently, more attention is being drawn towards the collaboration between humans and agents. Across the many fields where such an agent can be employed, we are going to specifically examine the domain of negotiation. A critical part to ensure success in this human-agent collaboration is to establish trust between the user and the agent. When considering negotiations, different strategies can be employed by the negotiation agents, the specific negotiation agent we shall use in our study employs the so called "hardliner negotiation style", which can be thought of as taking an uncompromising stance. When introducing the agent, we will either show a neutral introduction which does not mention the choice of strategy or show the hardliner introduction which does cover the chosen strategy. We are going to investigate how the choice of introduction affects the trust of humans in the negotiation agent. Leading us to the research question: What's the effect of an agent using a truthful explanation of their hardliner negotiation style versus a neutral explanation on how much humans trust and rely on the agent to negotiate for them? A between-subjects study took place to answer the question which has indicated that there is no significant difference (U = 111, p = 0.97 two-tailed, α = 0.05) between the two different introductions.

As autonomous systems are increasingly integrated as a team member for collaborative tasks, trust in human-agent teams (HAT) becomes crucial to foster success. In many real world scenarios, trust violations are expected, thus demanding the use of trust repair strategies to restore damaged trust. Previous research has shown that expressing regret and providing explanations are effective strategies to rebuild human-agent trust.
However, the role of various team dynamics, such as interdependence relationships, remains unexplored. The aim of this paper is to examine the influence of interdependence levels on the effectiveness of trust repair strategies. To investigate this, an experiment was conducted in a collaboration environment with an urban search and rescue mission. Two interdependence conditions were introduced to analyse their effect on trust and collaboration fluency.
No significant evidence was found to support a relationship between interdependence and trust repair or collaboration fluency. However, as this study only considers two interdependence conditions, there is much more room for future work to explore further. This study can bring meaningful insights to design and facilitate agents that are more trustworthy in human-agent collaboration settings.

Nowadays, Human Autonomy Teams (HATs) are incorporated in many fields, where humans and autonomous agents work collaboratively to combine their capabilities with the ultimate goal of performing tasks more efficiently. In such environments, it is imperative to sustain a high level of trust between the agents as collaboration is not possible without mutual trust. Naturally, this implies that recovering trust following trust violation is also a crucial aspect of HATs. Moreover, besides team performance, the fluency of collaboration is another important factor to consider when evaluating the success of the teams. This paper aims to investigate the effect of opportunistic (soft) interdependence between the agents on trust violation, trust repair, and on collaboration fluency when compared against a baseline (complete independence) condition. In this paper, interdependence relationships refer to how the agents complement/combine each other's competence. The experimental results were obtained through a user study, using questionnaires and logged objective metrics. Our research found that teams with opportunistic interdependence relationships were significantly affected by trust violations compared to the baseline condition. Furthermore, although not as significant as the effect of trust violation, they also experienced a significant trust recovery during the tasks. Finally, the results of analyzing both subjective and objective fluency metrics did not give any significant result that indicates the difference in the level of collaboration fluency between the two conditions.

Negotiation is a challenging process for people, which often results in suboptimal agreements between the negotiating parties. This issue leads to lost benefits that one of the negotiating parties could have obtained. To counteract this drawback artificially intelligent negotiation agents are developed. Their goal is to help negotiating parties increase the overall benefits from a negotiation. However, the main drawback of such agents is that people need to trust the agents in order to use them. In this research paper we investigate whether a negotiating agent with a hardliner negotiating style would be trusted more than a negotiating agent with a conceder negotiation style. We answer this question by conducting an user study, where we measure participants' trust in the two negotiation agents and compare the results. From the results we concluded that the assumption is correct for the sample group that participated in the user study.

With the growing importance of teamwork in higher education, effective communication and goal congruence have become vital in improving the effectiveness of student teamwork. This study aims todesign and implement an innovative system that combines a goal-setting chatbot and an effort visualizer to facilitate effective collaboration in student teams. We found that unclear goals and lack ofengagement were the main challenges in teamwork. To address these challenges, we developed asystem that combines a chatbot and an effort visualizer designed to enhance team transparency andgoal alignment through technology. The chatbot guides users to set specific collaborative goals throughthe SMART framework. At the same time, the effort visualizer displays each member’s contribution,thus increasing accountability among team members and facilitating greater participation. The systemwe designed was validated in a creative writing task (N=84) in a collaborative scenario. The resultsshowed that either the chatbot or the effort visualizer alone positively impacted student engagementand collaboration. However, when using the chatbot and effort visualizer in combination, there neededto be more clear evidence to improve engagement and collaboration effects further. This finding suggests that we pay more attention to the characteristics of individual tools and combination strategieswhen designing collaboration enhancement tools. This study provides valuable insights into using chatbots and visualization techniques to improve team collaboration and suggests new directions for futureresearch in educational technology.

As technology advances, automated systems become more autonomous which leads to a higher interdependence between machine and human. Much research has been done about trust between humans and trust of humans regarding machines. An interesting question that remains is how the behavior of an agent influences human trustworthiness in a human-agent collaborative setting. The research presented by this paper contributes to the understanding of this area. It investigates a specific behavioral trait using the following hypothesis: friendly behavior of an agent improves human trustworthiness. Here, trustworthiness is broken up in the constructs: ability, benevolence and integrity.

An experiment has been conducted using a collaborative Search and Rescue game. The following behaviors of the participants have been measured:
- Ability: speed and effectiveness;
- Benevolence: communication, willingness to help, agreeableness to advice, responsiveness;
- Integrity: truthfulness.
Furthermore, a likert scale has been used to measure the participants' own perception of their trustworthiness. The experiment is conducted with 20 participants in the control group, where the agent spoke in a neutral manner, and 20 in the experimental group, where the agent instilled empathy, stimulated collaboration, encouraged the participants and was affectionate.

The research has shown a significant improvement in the experimental group only for communication and willingness to help. This gives some indication that a friendly agent only slightly improves the trustworthiness of a human. However, the research has some limitations that might also explain the lack of significant results. Firstly, it is unclear to what extent the measures truly measured the constructs of trustworthiness. Secondly, to create a friendly agent, theories from organizational and social psychology are used, which are mostly focussed on human-human relationships, instead of human-agent relationships. Finally, Some confounding variables may have had an impact, like lag in the game and the participant not properly reading the agent’s messages.

Artificial Intelligence (AI) is increasingly helping people with all kinds of tasks, due to its promising capabilities. In some tasks, an AI system by itself will take over tasks, but in other tasks, an AI system making decisions on its own would be undesired due to ethical and legal reasons. In those cases, AI can still be of help by forming human-AI teams, in which humans get advice from the AI system helping them with making their final decisions. Human-AI teams are for instance used in the medical and legal fields. One problem arises, in which instances should one trust an AI system and in which not? Trusting the AI system when it is correct and trusting yourself when you are correct, results in a high appropriate reliance. If users appropriately rely on AI systems, it is possible to achieve complementary team performance, which is better than any single teammate. However, as known from previous literature, people struggle with assessing their performance and knowing how well they perform compared to peers. When one overestimates their performance this can be because of a dual burden, due to the lack of skill they also lack the skill to accurately estimate their performance. This phenomenon is called the Dunning-Kruger Effect (DKE). This raises questions about whether the inability to estimate their own capabilities would also reflect on their assessment of the AI system its performance.

In this thesis we look at how the DKE affects (appropriate) reliance on AI systems and if so, how such effects due to the DKE can be mitigated. The effects of the DKE and possible mitigation are being tested through an empirical study (N = 249). The attempt at mitigation is done by including a tutorial intervention, which has been proved in previous research to be useful in decreasing the DKE. The tutorial intervention is aimed at revealing the weaknesses of the participant and making them aware of their miscalibrated self-estimation. Furthermore, in this thesis, the effects of revealing how the AI system makes its decisions through explainable AI (XAI) are explored. The XAI consisted of highlights from logic unit-based explanations, it should allow participants to gain more understanding of the AI advice. This thesis shows how this will affect user self-assessment and reliance on the AI system.

We found that participants who overestimate themselves tend to rely less on the AI system, compared to participants that had an accurate or underestimation of their performance. After the tutorial participants have a better calibration of their self-assessment. While the designed tutorial intervention can help participants calibrate their self-assessment, it fails to promote (appropriate) reliance. Furthermore, the logic units-based explanations did not improve accurate self-assessing or increase user (appropriate) reliance on AI systems.
This thesis shows the importance of considering cognitive biases when dealing with human-AI teams and invites more research on how to handle and mitigate the DKE in human-AI decision making.

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.

Working with pneumatics can be done using a Programmable Logic Controller (PLC), which commands the individual actuators. To start, the movements of each component are described in a displacement diagram. From this diagram, ladder logic can be extracted, which is a set of conditions with corresponding actions. These actions are executed when the conditions hold. This ladder logic is the programming language of PLCs and makes it possible to control pneumatics. De Techniekschool provides courses on these techniques and pneumatics.

This report describes the research, design and implementation of a software application to aid students in their education of pneumatics. The main goal is to allow users to create a displacement diagram and then generate the corresponding ladder logic based on this diagram. This will allow users to understand how this conversion works and help them in understanding PLC programming.

In the research phase, various designs were evaluated. The implementation of the compiler was thought out and possible designs for the Graphical User Interface (GUI) for creating the diagram were explored. In the case of the GUI, the final implementation differs from the initial chosen design. This is because, during the project, it was discovered that the design did not work as well as intended, thus the switch was made. To guarantee the correct behaviour of the product, each individual component was tested on its own with unit tests.

Besides a compiler and the ability to draw displacement diagrams, features of Stepladder include the option to save and open work, and the option to undo and redo actions. It is also possible to show the signals which the PLC will receive and send. These signals already play a vital role in the conversion from displacement diagram to ladder logic and the generated signals can help to understand this conversion.

Desirable features which are not yet implemented are the support for multiple languages and improvements to the accessibility of the application. Also, a more advanced compiler is desired, because the current compiler does create ladder logic according to a valid technique, but it is not the optimal solution. For learning purposes, an optimal conversion may be desired. Proper user testing is also desired to verify the design and whether the application is easy to use.

To conclude, the final product allows the user to create displacement diagrams and generate ladder logic. This can be used to study the conversion needed in PLC programming. The final product meets the design goals set in the design process. Besides the design goals, the product also satisfies the initial requirements. There are some features the client could implement but these are not required.

Most of the content creation applications that are currently common in use are regular PC applications with simulated 3D visualisation on a 2D screen and indirect interaction through a mouse and keyboard. Augmented Reality (AR) is a medium that can provide actual 3D visualisation and more hands-on interaction for these applications. This thesis explores how AR can be used for story authoring, a specific type of content creation. Both types of existing AR interfaces, tangible and touch-less were explored and combined in a useful way. The Story ARtist application was developed to evaluate the designed interactions and AR visualisation for story authoring. Its interface combines tangible and touch-less interactions in the form of physical markers and hand tracking input. A tabletop environment is used to visualise the story authoring elements dynamically, using the 3D space that AR provides. Story authoring is kept simple for accessibility, with a plot point structure focused on core story elements like actions, characters and objects. A user study was done with the concept application to evaluate the designed AR interaction and visualisation for story authoring. The results show that AR has considerable potential for story authoring.

With the advent of social robots which are designed to be 'social', human-like interactions have become a necessity. It is natural for us to use a plethora of emotions to convey additional information or to make an interaction more engaging. But emotion expression is not commonly associated with robots. Many humanoid robots cannot generate facial expressions to portray various emotions. Studies have shown that robots are multi-modal systems which can employ multiple channels to express an emotion. This thesis explores the non-verbal emotion expression techniques and their expressive capabilities. We found that some emotions are easier to express than others, and a single technique cannot express all the emotions. We chose a few emotions and systematically determined the best technique for each of them.

This project focuses on finding what defines an appropriate moment to notify in a smart reminder system. Specifically, the goal is to find a way in which smart reminders systems can be extended through the use of user values to ultimately provide more appropriately timed reminders. This is essential in providing software aided support. A system is designed from scratch, combining existing concepts of activity prediction and value-based design. A statistical Markov chain model is made from predictions based on Expectation Maximization and Apriori algorithms. User values are quantified and optimized following the concept of a Socially Adaptive Electronic Partner and added to the model to identify an appropriate moment for a notification. The concept of values is broken down into two aspects. Firstly, the value loss invoked by the nuisance of receiving a notification during a certain activity. Secondly, the expected value gain achieved by actually remembering is simulated through the expected time between the moment of notification and the deadline. The model is implemented in a Node.js web application, following the principles of a RESTful web API. The model is tested for both its success in correct prediction and the moment selection. The basic predictive model shows a 91% success rate but falls short at 73% when considering values. After optimizing the system for user values, up to a 13% improvement in the success rate and an 18% improvement in the score (more appropriate moment) is found for the model considering user values with respect to the basic, predictive model. Overall, a clear and workable approach to value-based smart reminders is shown through a statistical and dynamic approach to incorporate the concept of user values in a smart-reminder system.