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 …

We propose a simple approach to combining first-order logic and probabilistic graphical
models in a single representation. A Markov logic network (MLN) is a first-order knowledge …

Handbook of Knowledge Representation describes the essential foundations of Knowledge
Representation, which lies at the core of Artificial Intelligence (AI). The book provides an up …

There have been many proposals for first-order belief networks (ie, where we quantify over
individuals) but these typically only let us reason about the individuals that we know about …

Interest in statistical relational learning (SRL) has grown rapidly in recent years. Several key
SRL tasks have been identified, and a large number of approaches have been proposed …

We describe in this paper a system for exact inference with relational Bayesian networks as
defined in the publicly available Primula tool. The system is based on compiling …

Publisher Summary A Bayesian network is a tool for modeling and reasoning with uncertain
beliefs; it comprises two parts: a qualitative component in the form of a directed acyclic graph …

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 …

We introduce a new approach to probabilistic logic programming in which probabilities are
defined over a set of possible worlds. More precisely, classical program clauses are …

Reasoning about graphs, and learning from graph data is a field of artificial intelligence that
has recently received much attention in the machine learning areas of graph representation …