Humans and machines harmoniously collaborating and benefiting from each other is a long lasting dream for researchers in robotics and artificial intelligence. An important feature of efficient and rewarding cooperation is the ability to assume possible problematic situations and act in advance to prevent negative outcomes. This concept of assistance is known under the term proactivity. In this article, we investigate the development and implementation of proactive dialogues for fostering a trustworthy human-computer relationship and providing adequate and timely assistance. Here, we make several contributions. A formalisation of proactive dialogue in conversational assistants is provided. The formalisation forms a framework for integrating proactive dialogue in conversational applications. Additionally, we present a study showing the relations between proactive dialogue actions and several aspects of the perceived trustworthiness of a system as well as effects on the user experience. The results of the experiments provide significant contributions to the line of proactive dialogue research. Particularly, we provide insights on the effects of proactive dialogue on the human-computer trust relationship and dependencies between proactive dialogue and user specific and situational characteristics.

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.

Current research on conversational recommender systems (CRS) focuses mainly on single-user interactions, neglecting situations where multiple users need to receive suggestions on a shared goal. This requires a CRS to conduct a multi-party dialogue and participate in the negotiation process, which presents various research challenges. In this paper, we investigate how actively a CRS needs to participate in a multi-party negotiation dialogue by conducting a WoZ study. The study compares conversational strategies of a CRS – conversation-leading and -following – regarding task effectiveness and efficiency, user experience, and group dynamics. The user study highlighted both the advantages and disadvantages of employing a conversation-leading strategy in a CRS. The strategy sped up decisions and lessened chatbot moderation but had drawbacks: bias towards dominant individuals and lower group performance. Our findings also highlighted how users’ openness to experience affected their views on the strategy’s perspicuity and dependability.

This paper investigates the evolving landscape of Multi-User Conversational Interfaces, addressing the limitations of traditional systems that primarily focus on single-user interactions. As real-world applications grow more complex, there is a pressing need for conversational systems capable of facilitating dialogues among multiple participants. Our systematic survey reviews recent advancements in the field, highlighting innovative architectures, application domains, user and dialogue modelling, and evaluation metrics tailored for multi-user contexts. We conduct a comprehensive analysis of relevant literature, employing both quantitative and qualitative methodologies to identify common patterns and challenges in multi-user interactions. The findings underscore the importance of developing robust interfaces that can effectively manage overlapping dialogues, ensure collaborative group work, and enhance overall conversational quality. This work contributes to the understanding of Multi-User Conversational Interfaces and may help as a basis for future research aiming to develop more natural, user-friendly, and effective conversational interfaces.

Nicolas Wagner, Stefan Ultes. Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue. 2024.