When a user interacts with a conversational agent for the first time, they may not be aware of the agent’s capabilities, leading to suboptimal use or interaction breakdowns. To avoid a mismatch with the actual capabilities, the agent’s capabilities have to be made transparent to the user. To investigate whether communication of an agent’s capabilities during interactions enhances transparency and improves the user’s mental model, we conducted a user study with 56 participants. Each participant had three speech-based interactions with an agent that communicated its capabilities or an agent that did not. Our results suggest that the communication led to a change in user behavior with significantly longer utterances. However, the users’ mental models of the agent’s capabilities were not significantly different between the conditions. Participants were able to significantly improve their knowledge of the agent’s capabilities by aligning their mental model over time in both conditions.

As social robots see increasing deployment within the general public, improving the interaction with those robots is essential. Spoken language offers an intuitive interface for the human–robot interaction (HRI), with dialogue management (DM) being a key component in those interactive systems. Yet, to overcome current challenges and manage smooth, informative, and engaging interaction, a more structural approach to combining HRI and DM is needed. In this systematic review, we analyze the current use of DM in HRI and focus on the type of dialogue manager used, its capabilities, evaluation methods, and the challenges specific to DM in HRI. We identify the challenges and current scientific frontier related to the DM approach, interaction domain, robot appearance, physical situatedness, and multimodality.