Exploiting Prior Knowledge in Intelligent Assistants: Combining Relational Models with Hierarchies

Statitsical relational models have been successfully used to model static probabilistic relationships between the entities of the domain. In this talk, we illustrate their use in a dynamic decison-theoretic setting where the task is to assist a user by inferring his intentional structure and taking appropriate assistive actions. We show that the statistical relational models can be used to succintly express the system's prior knowledge about the user's goal-subgoal structure and tune it with experience. As the system is better able to predict the user's goals, it improves the effectiveness of its assistance. We show through experiments that both the hierarchical structure of the goals and the parameter sharing facilitated by relational models significantly improve the learning speed.