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 …

Advanced statistical modeling and knowledge representation techniques for a newly
emerging area of machine learning and probabilistic reasoning; includes introductory …

Probabilistic models provide a sound and coherent foundation for dealing with the noise and
uncertainty encountered in most real-world domains. Bayesian networks are a language for …

Probabilistic Logic Programming extends Logic Programming by enabling the
representation of uncertain information by means of probability theory. Probabilistic Logic …

We present CLP (BN), a novel approach that aims at expressing Bayesian networks through
the constraint logic programming framework. Arguably, an important limitation of traditional …

Log-linear description logics are a family of probabilistic logics integrating various concepts
and methods from the areas of knowledge representation and reasoning and statistical …

While probabilistic logics in principle might be applied to solve a range of problems, in
practice they are rarely applied-perhaps because they seem disparate, complicated, and …

Probabilistic relational models (PRMs) are a rich representation language for structured
statistical models. They combine a frame-based logical representation with probabilistic …

In this chapter, we provide a detailed description of the Markov logic representation. We
begin by providing background on first-order logic and probabilistic graphical models and …

The past few years have seen a surge of interest in the field of probabilistic logic learning
and statistical relational learning. In this endeavor, many probabilistic logics have been …