First-Order Probabilistic Inference

Many Artificial Intelligence (AI) tasks, such as natural language processing, commonsense reasoning and vision, could be naturally modeled by a language and associated inference engine using both relational (first-order) predicates and probabilistic information. While logic has been the basis for much AI development and is a powerful framework for using relational predicates, its lack of representation for probabilistic knowledge severely limits its application to many tasks. Graphical models and Machine Learning, on the other hand, can capture much of probabilistic reasoning but lack convenient means for using relational predicates.

In the last fifteen years, many frameworks have been proposed for merging those two approaches but have mainly been probabilistic logic languages resorting to propositionalization of relational predicates (and, as a consequence, ordinary graphical models inference). This has the severe disadvantage of ignoring the relational structure of the model and potentially causing exponential blowups in inference time.

I will talk about my work in integrating logic and probabilistic inference in a more seamless way. This includes Lifted First-Order Probabilistic Inference, a way of performing inference directly on first-order representation, without propositionalization, and work on DBLOG (Dynamic Bayesian Logic), an extension of BLOG (Bayesian Logic, by Milch and Russell) for temporal models such as data association and activity recognition. I will conclude with what I see as important future directions in this field.