An intelligent agent interacting with the real world will encounter individual people, courses,
test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of …

Many representation schemes combining first-order logic and probability have been
proposed in recent years. Progress in unifying logical and probabilistic inference has been …

Probabilistic logical languages provide powerful formalisms for knowledge representation
and learning. Yet performing inference in these languages is extremely costly, especially if it …

Lifted graphical models provide a language for expressing dependencies between different
types of entities, their attributes, and their diverse relations, as well as techniques for …

Many AI problems arising in a wide variety of fields such as machine learning, semantic
web, network communication, computer vision, and robotics can elegantly be encoded and …

Probabilistic logics are receiving a lot of attention today because of their expressive power
for knowledge representation and learning. However, this expressivity is detrimental to the …

Probabilistic data is motivated by the need to model uncertainty in large databases. Over the
last twenty years or so, both the Database community and the AI community have studied …

Exchangeability is a central notion in statistics and probability theory. The assumption that
an infinite sequence of data points is exchangeable is at the core of Bayesian statistics …

Lifted probabilistic inference algorithms exploit regularities in the structure of graphical
models to perform inference more efficiently. More specifically, they identify groups of …

Tractable subsets of first-order logic are a central topic in AI research. Several of these
formalisms have been used as the basis for first-order probabilistic languages. However …