Dialogue systems for the health domain are expected to be reliable and to reason in accordance to medical experts' reasoning. Given the complexities of the health domain, these systems are frequently aided by semantic-aware approaches implementing technologies such as ontologies. However, the automated generation of such systems is still a challenging task. In this work, we propose an approach that integrates automated planning and information management with the aim of automating the generation of efficient dialogue managers. Resulting dialogue managers are capable of handling multi-turn goal-oriented dialogue sessions within the healthcare domain. By evaluating a prototype on the asthma domain, our results reveal the suitability of our approach to generate dialogue policies on real-time scenarios.

Providing reminders to elderly people in their home environment, while they perform their daily activities, is considered as a user support activity, and thus a relevant topic in Active and Assisted Living (AAL) research and development. Determining such reminders implies decision-making, since the actions' flow (behavior) usually involves probabilistic branches. An automated system needs to decide which of the next actions is the best one for the user in a given situation. Problems of this nature involve uncertainty levels that have to be dealt with. Many approaches to this problem exploit statistical data only, thus ignoring important semantic data as, for instance, are provided by Ontologies. However, ontologies do not support reasoning over uncertainty natively. In this paper, we present a probabilistic semantic model that enables reasoning over uncertainty without losing semantic information. This model will be exemplified by an extension of the Human Behavior Monitoring and Support [HBMS] approach that provides a conceptual model for representing the user's behavior and its context in her/his living environment. The performance of this approach was evaluated using real data collected from a smart home prototype equipped with sensors. The experiments provided promising results which we will discuss regarding limits and challenges to overcome.

Context-awareness proposes that computer systems should use the information from the environment to adapt their execution and content to benefit the user. With the recent adoption of Massive Open Online Courses by educational institutions, the use of context information can be a determining factor in course retention and learning effectiveness. In this study, we modeled an ontology network for the adaptation and suggestion of materials to students based on context information, using a health course in the area of Motivational Interviewing taught in OpenEDX platform. Through a validation of this ontology network in a case study, it was found that the ontology network offered the possibility of recommending materials based on context information. The recommendations were made through the inference based on a set of rules defined in the ontology network.