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\u27s prior knowledge about the user\u27s goal-subgoal structure and tune it with experience. As the system is better able to predict the user\u27s 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

Relational Boosted Bandits

Contextual bandits algorithms have become essential in real-world user interaction problems in recent years. However, these algorithms rely on context as attribute value representation, which makes them unfeasible for real-world domains like social networks are inherently relational. We propose Relational Boosted Bandits(RB2), acontextual bandits algorithm for relational domains based on (relational) boosted trees. RB2 enables us to learn interpretable and explainable models due to the more descriptive nature of the relational representation. We empirically demonstrate the effectiveness and interpretability of RB2 on tasks such as link prediction, relational classification, and recommendations.Comment: 8 pages, 3 figure