LAUSR

We propose a computational model that endows conversational agents with the capability to coordinate their speaking turns (turn-taking management) in the context of mixed-initiative two-party dialogs. In human conversations, participants are continuously adjusting their verbal and non-verbal productions for ensuring the effective coordination of speaking turns. In our model, the decision making is a continuous process based on the intrinsic current goal of the agent with respect to turn-taking, namely its motivation to keep-or to leave-its current role (speaker or listener), and on its perception of the intentions of its partner. Concurrently, the agent is also producing signals indicating its willingness to maintain or leave its current role. Our model is based on two models from cognitive psychology: the drift-diffusion model and the theory of behavioral dynamics. After presenting simulations showing how our model makes the coordination emerge from the interactions, we propose a SAIBA-Compliant architecture, named BeAware, created to support the implementation of our model. Finally, using our model, we investigate how an agent’s turn-taking strategy may impact the user’s experience and the effectiveness of the coordination.