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 …

A multitude of different probabilistic programming languages exists today, all extending a
traditional programming language with primitives to support modeling of complex, structured …

Although classical first-order logic is the de facto standard logical foundation for artificial
intelligence, the lack of a built-in, semantically grounded capability for reasoning under …

We present a method for parameter learning in relational Bayesian networks (RBNs). Our
approach consists of compiling the RBN model into a computation graph for the likelihood …

Dependency networks approximate a joint probability distribution over multiple random
variables as a product of conditional distributions. Relational Dependency Networks (RDNs) …

State abstraction is necessary for better task transfer in complex reinforcement learning
environments. Inspired by the benefit of state abstraction in MAXQ and building upon hybrid …

Judging by the increasing impact of machine learning on large-scale data analysis in the
last decade, one can anticipate a substantial growth in diversity of the machine learning …

Intention recognition is the process of becoming aware of the intentions of other agents,
inferring them through observed actions or effects on the environment. Intention recognition …

Recent years have seen a surge of interest in Statistical Relational Learning (SRL) models
that combine logic with probabilities. One prominent and highly expressive SRL model is …

We introduce an extension of the multi-instance learning problem where examples are
organized as nested bags of instances (eg, a document could be represented as a bag of …