Catch Prof. Sriraam Natarajan teach Human Allied Statistical Relational AI at ACAI 2018 Summer School on Statistical Relational Artificial Intelligence on August 27-31, 2018 at Ferrara, Italy.

Statistical Relational AI (StaRAI) Models combine the powerful formalisms of probability theory and first-order logic to handle uncertainty in large, complex problems. While they provide a very effective representation paradigm due to their succinctness and parameter sharing, efficient learning is a significant problem in these models. First, I will discuss state-of-the-art learning methods based on boosting that is representation independent. Our results demonstrate that learning multiple weak models can lead to a dramatic improvement in accuracy and efficiency.

One of the key attractive properties of StaRAI models is that they use a rich representation for modeling the domain that potentially allows for seam-less human interaction. However, in current StaRAI research, the human is restricted to either being a mere labeler or being an oracle who provides the entire model. I will present the recent progress that allows for more reasonable human interaction where the human input is taken as “advice” and the learning algorithm combines this advice with data. Finally, I will discuss more recent work on soliciting advice from humans as needed that allows for seamless interactions with the human expert.