A Planning Strategy For Dialogue Management in Healthcare

Planning strategies aim to support an agent in achieving a specific goal. Similarly, dialogue-based strategies are thought for (i) equipping intelligent systems with data-acquisition capabilities, and (ii) supporting users by providing, for example, task orientation. In the digital health domain, the interactions between physicians and patients have the objective of classifying the patients current condition to, subsequently, give him/her new instructions. This work aims to develop a non-deterministic planning based approach for human-machine dialogue management in the health domain. The approach is divided in two parts: the first part focuses on slot-filling dialogues for acquiring information about the patient in order to classify him/her with respect to clusters. The second part concerns planning for the management of a task-oriented dialogue to give advice to patients with the intention of giving some guidance on the patient’s treatment. Both parts are supported by a knowledge-based back-end performing reasoning every time new data are provided by users. For demonstrating the complexity of this task, we provide a sample scenario based on the asthma domain showing how, even with few variables, the management of a whole conversation is challenging.