This research paper focuses on the accuracy and limitations of user values elicited through a textual interface with questions asked in isolation. The primary objective was to conduct a user study using a textual interface that uses questions in isolation to assess the effectiveness and accuracy of this interface type and questioning style. The study involved exploring various scenarios and associated user values, as well as comparing the textual interface with graphical and audio interfaces tested in four related studies. The user study consisted of 15 participants who interacted with the textual interface. Afterwards, they were tasked with judging and adapting their behaviour models while also evaluating the interface's usability. The findings indicate that while the textual interface demonstrates decent usability, participants did not perceive a strong need for the current system and that compared to other interface types, the textual interface does not yield the most accurate results. This research provides insights into the usability and limitations of a textual interface for eliciting user values. It emphasizes the need for further exploration and development of alternative interface types to enhance accuracy and user engagement.

Individuals seeking a healthier lifestyle can benefit from behavior support agents. Customization and transparency are crucial for system effectiveness. This paper proposes using behavior trees as a user model, with a conversational agent extracting necessary information. The conversational interface enhances transparency, allowing users to understand how the system perceives them. Understanding comparative questions is vital to this approach's success. The objective is to investigate modeling personal values accurately using a conversational agent. Technologically literate participants engaged in iterative dialogue to elicit a personalized user model. Scenarios explored contextual factors' impact on value alignment. Results revealed decreased accuracy when more values were affected by contextual factors. Comparative questions were less effective than isolated questioning. System usability was rated poor but approaching acceptability. Larger sample sizes are needed for comprehensive conclusions. This research lays the foundation for conversational agents that model personal values within behavior trees, advancing behavior support systems.

The alignment of behavior support systems with our personal values becomes increasingly important as behavior support systems continue to influence our daily lives. The purpose of this paper was to explore the use of graphical interfaces and isolation questions to elicit personal values and build accurate user models. An experiment was conducted in two phases to assess the accuracy and usability of the created interface. A comparison was also made with other types of interfaces developed within the research group. This experiment provided valuable insights but also held some limitations. The findings form a valuable contribution for future research and development in building responsible AI and personalized assistance from behavior support agents.

Behavior support applications aim to provide personalized and flexible support to users in various domains. To achieve this, understanding users' preferences, values, and context is crucial. Creating user models that incorporate users' norms and values has been proposed as a solution to capture the relationship between desired behaviors and values. However, updating and modifying user models at run-time remains a challenge, as users' norms and values may change over time. This study investigates the accuracy of an audio interface designed to elicit values-related information using isolated questions. This involves designing an audio interface and evaluating its effectiveness through participant interactions, where they are presented with four scenarios. It was found that the audio interface performs above average in terms of usability, as indicated by the System Usability Scale score. The accuracy of the user models is evaluated through the Hamming distance and value differences between the base model and the participant-improved model. Most models required a small number of changes, and when changes were made, they were generally minimal. Additionally, feedback collected through open-ended interview questions lays down a basis for further development. The study contributes to the field by demonstrating the efficacy of the audio interface and its potential for updating user models in real-time. Overall, the research findings support the development of more effective and personalized behavior support applications that can adapt to its users.

Behavior support applications require an understanding of the user's values to provide personalized support. User models have been developed to capture this relationship, but they may not always address unforeseen circumstances due to the changing nature of human values. To address this, the ability to update value models in real-time is crucial. This paper presents a study on the design of a graphical user interface (GUI) that utilizes behavior trees to elicit and update personal values. The study focuses on comparing the accuracy and usability of different questioning techniques, specifically comparative questions and isolation questions, and user interfaces. The results show that GUI with isolation questions and text user interface with comparative questions performed better in terms of accuracy compared to GUI with comparative questions. The study also highlights the possible impact of discrepancies in experimental procedures on the results. Furthermore, the limitations of the behavior tree model and the need for model improvements are discussed. Future work is proposed to address these limitations and explore enhancements such as alternative models, supervised processes for baseline tree building, frictionless elicitation techniques, consideration of random value shifts and big life events, and the potential of large language models. Overall, the findings contribute to the understanding of eliciting and updating user value models in personalized systems.