In this paper, we present an approach for extracting knowledge graph information for retrieval augmented generation in dialogue systems. Knowledge graphs are a rich source of background information, but the inclusion of more potentially useful information in a system prompt risks decreased model performance from excess context. We investigate a method of retrieving relevant subgraphs of maximum relevance and minimum size by framing this trade-off as a Prize-collecting Steiner Tree problem. The results of our user study and analysis indicate promising efficacy of a simple subgraph retrieval approach compared with a top-K retrieval model.

This paper investigates the enhancement of Dialogue Systems by integrating the creative capabilities of Large Language Models.While traditional Dialogue Systems focus on understanding user input and selecting appropriate system actions, Language Models excel at generating natural language text based on prompts.Therefore, we propose to improve controllability and coherence of interactions by guiding a Language Model with control signals that enable explicit control over the system behaviour.To address this, we tested and evaluated our concept in 815 conversations with over 3600 dialogue exchanges on a dataset.Our experiment examined the quality of generated system responses using two strategies: An unguided strategy where task data was provided to the models, and a controlled strategy in which a simulated Dialogue Controller provided appropriate system actions.The results show that the average BLEU score and the classification of dialogue acts improved in the controlled Natural Language Generation.

The next step for intelligent dialog agents is to escape their role as silent bystanders and become proactive. Well-defined proactive behavior may improve human-machine cooperation, as the agent takes a more active role during interaction and takes off responsibility from the user. However, proactivity is a double-edged sword because poorly executed pre-emptive actions may have a devastating effect on the task outcome and the relationship with the user. For designing adequate proactive dialog strategies, we propose a novel approach including both social and task-relevant features in the dialog. Here, the primary goal is to optimize proactive behavior so that it is task-oriented - this implies high task success and efficiency - while also being socially effective by fostering user trust. Including both aspects in the reward function for training a proactive dialog agent using reinforcement learning showed the benefit of our approach for more successful human-machine cooperation.

In this work, we present a robot-dialogue framework to handle sophisticated robot-initiated interaction. We introduce a robotic assistant equipped with a dialogue system in a household assistance context. To become a truly collaborative companion, the assistant is able to engage in a proactive conversation for task assistance. The system actions are triggered by the recognition of persons or specific objects. To evaluate our system, we conducted a user study with 17 participants in a laboratory environment where users were able to interact with the system via natural language. The results showed that the behaviour of the system was accepted and perceived as trustworthy by the users.

Robots will eventually enter our daily lives and assist with a variety of tasks. Especially in the household domain, robots may become indispensable helpers by overtaking tedious tasks, e.g. keeping the place tidy. Their effectiveness and efficiency, however, depend on their ability to adapt to our needs, routines, and personal characteristics. Otherwise, they may not be accepted and trusted in our private domain. For enabling adaptation, the interaction between a human and a robot needs to be personalized. Therefore, the robot needs to collect personal information from the user. However, it is unclear how such sensitive data can be collected in an understandable way without losing a user’s trust in the system. In this paper, we present a conversational approach for explicitly collecting personal user information using natural dialogue. For creating a sound interactive personalization, we have developed an empathy-augmented dialogue strategy. In an online study, the empathy-augmented strategy was compared to a baseline dialogue strategy for interactive personalization. We have found the empathy-augmented strategy to perform notably friendlier. Overall, using dialogue for interactive personalization has generally shown positive user reception.

Trust forms an important factor in human-robot interaction and is highly influencing the success or failure of a mixed team of humans and machines. Similarly, to human-human teamwork, communication and proactivity are one of the keys to task success and efficiency. However, the level of proactive robot behaviour needs to be adapted to a dynamically changing social environment. Otherwise, it may be perceived as counterproductive and the robot’s assistance may not be accepted. For this reason, this work investigates the design of a socially-adaptive proactive dialogue strategy and its effects on humans’ trust and acceptance towards the robot. The strategy is implemented in a human-like household assistance robot that helps in the execution of domestic tasks, such as tidying up or fetch-and-carry tasks. For evaluation of the strategy, users interact with the robot while watching interactive videos of the robots in six different task scenarios. Here, the adaptive proactive behaviour of the robot is compared to four different levels of static proactivity: None, Notification, Suggestion, and Intervention. The results show that proactive robot behaviour that adapts to the social expectations of a user has a significant effect on the perceived trust in the system. Here, it is shown that a robot expressing socially-adaptive proactivity is perceived as more competent and reliable than a non-adaptive robot. Based on these results, important implications for the design of future robotic assistants at home are described.

In mixed-initiative user interactions, a user and an autonomous agent collaborate for solving tasks by taking interleaving actions. However, this shift of control towards the agent requires a formation of trust for the user, otherwise the assistance possibly will be rejected and becomes obsolete. One approach for fostering a trustworthy interaction is to equip an agent with proactive dialogue capabilities. However, the development of adequate proactive dialogue strategies is complex and highly user- as well as context-dependent. Inappropriate usage of proactive conversation may even do more harm than good and corrupt the human-computer trust relationship. In order to alleviate this problem, modelling and predicting a proactive system’s perceived trustworthiness during an ongoing interaction is essential. Therefore, this paper presents novel work on the development of a user model for live prediction of trust during proactive interaction, incorporating user-, system-, and context-dependent features. For predicting trust, three machine-learning algorithms – support vector machine, eXtreme Gradient Boost, gated recurrent unit network – are trained and tested on a proactive dialogue corpus. The experimental results show that among the classifiers the support vector machine showed the most well-rounded performance, while the gated recurrent unit had the best accuracy. The results prove the developed user model to be reliable for predicting trust in proactive dialogue. Based on the outcomes, the usability of the proposed method in real-life scenarios is discussed and implications for developing user-adaptive proactive dialogue strategies are described.

Intelligent computer systems aim at providing user-assistance for challenging tasks, like decision-making, planning, or learning. For offering optimal assistance, it is essential for such systems to decide when to be reactive or proactive and how active system behaviour should be designed. Especially, as this decision may greatly influence the user's trust in the system. Therefore, we conducted a mixed-factorial study which examines how different levels of proactivity (none, notification, suggestion, and intervention) as well as timing strategies (fixed-timing and insecurity-based) are trusted by subjects while performing a planning task. The results showed, that proactive system behaviour is perceived trustworthy in insecure situations independent of the timing. However, proactive dialogue showed strong effects on cognition-based trust (system's perceived competence and reliability) depending on task difficulty. Furthermore, fully autonomous system behaviour fails to establish an adequate human-computer trust relationship, in contrast to conservative strategies.