Navigating and Reaching Therapeutic Goals with Dynamical Systems in Conversation-Based Interventions

Modern human behavioral signal processing and machine-learning methods have introduced novel ways for representing and estimating internal states of people in goal-based conversational interactions, such as psychotherapy. By combining these methods with systems theoretic approaches, we demonstrate how canonical approaches to control policy design can be utilized for improving the quality of goal-oriented talk-based interactions.